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Xueyang Fu

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31 papers

2 author rows

AAAI Conference 2026 Conference Paper

CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring

Mingchen Zhong
Xin Lu
Dong Li
Senyan Xu
Ruixuan Jiang
Xueyang Fu
Baocai Yin

Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiveness against combined low-light and motion blur degradations. To overcome this, we propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration. CompEvent features two core components: 1) Complex Temporal Alignment GRU, which utilizes complex-valued convolutions and processes video and event streams iteratively via GRU to achieve temporal alignment and continuous fusion; and 2) Complex Space-Frequency Learning module, which performs unified complex-valued signal processing in both spatial and frequency domains, facilitating deep fusion through spatial structures and system-level characteristics. By leveraging the holistic representation capability of complex-valued neural networks, CompEvent achieves full-process spatiotemporal fusion, maximizes complementary learning between modalities, and significantly strengthens low-light video deblurring capability. Extensive experiments demonstrate that CompEvent outperforms SOTA methods in addressing this challenging task.

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AAAI Conference 2025 Conference Paper

A Lottery Ticket Hypothesis Approach with Sparse Fine-tuning and MAE for Image Forgery Detection and Localization

Jiaying Zhu
Dong Li
Xueyang Fu
Gege Shi
Jie Xiao
Aiping Liu
Zheng-Jun Zha

The rise in sophisticated image forgery techniques, driven by advancements in image editing and generation, has posed new security challenges. Traditional methods, designed for specific tampering artifacts, struggle with out-of-distribution image forgery detection. In this paper, we propose a shift in paradigm, placing greater emphasis on the universal characteristics of authentic images, as opposed to solely focusing on specific forgery signals. We introduce an enhancement to the Masked Autoencoder (MAE), aptly termed the Forgery MAE (FMAE). This modification retains the inherent characteristics of natural images while integrating multi-source forgery information. Our implementation involves applying the lottery ticket hypothesis during pre-training to identify forgery-sensitive parameters, followed by their sparse fine-tuning to target the forgery detection and localization task. Concurrently, we develop a ``mixture of experts'' noise extractor to compile multi-source forgery data. Our FMAE effectively extracts forgery features and shows strong resilience against unseen forgeries. Extensive experiments across multiple datasets confirm our method's superior accuracy and generalization capability over existing techniques.

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AAAI Conference 2025 Conference Paper

Boosting Image De-Raining via Central-Surrounding Synergistic Convolution

Long Peng
Yang Wang
Xin Di
PeizheXia
Xueyang Fu
Yang Cao
Zheng-Jun Zha

Rainy images suffer from quality degradation due to the synergistic effect of rain streaks and accumulation. The rain streaks are anisotropic and show a specific directional arrangement, while the rain accumulation is isotropic and shows a consistent concentration distribution in local regions. This distribution difference makes unified representation learning for rain streaks and accumulation challenging, which may lead to structure distortion and contrast degradation in the deraining results. To address this problem, a central-surrounding mechanism inspired Synergistic Convolution (SC) is proposed to extract rain streaks and accumulation features simultaneously. Specifically, the SC consists of two parallel novel convolutions: Central-Surrounding Difference Convolution (CSD) and Central-Surrounding Addition Convolution (CSA). In CSD, the difference operation between central and surrounding pixels is injected into the feature extraction process of convolution to perceive the direction distribution of rain streaks. In CSA, the addition operation between central and surrounding pixels is injected into the feature extraction process of convolution to facilitate the modeling of rain accumulation properties. The SC can be used as a general unit to substitute Vanilla Convolution (VC) in current de-raining networks to boost performance. To reduce computational costs, CSA and CSD in SC are merged into a single VC kernel by our parameter equivalent transformation before inferencing. Evaluations of twelve de-raining methods on nine public datasets demonstrate that our proposed SC can comprehensively improve the performance of twelve de-raining networks under various rainy conditions without changing the original network structure or introducing extra computational costs. Even for the current SOTA methods, SC can further achieve SOTA++ performance. The source codes will be publicly available.

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AAAI Conference 2025 Conference Paper

DCTMamba: Advancing JPEG Image Restoration Through Long-Sequence Modeling and Adaptive Frequency Strategy

Xi Wang
Xueyang Fu
Liang Li
Zheng-Jun Zha

Despite the advanced long-sequence modeling of Mamba, which has expanded its applications in image restoration, there remains a lack of exploration combining its strengths with the specific characteristics of JPEG image restoration, where high-frequency components are lost after the Discrete Cosine Transform (DCT). To address this, we introduce DCTMamba, a new framework designed to apply Mamba more effectively to JPEG image restoration. Specifically, our method integrates the Discrete Cosine Transform (DCT) into the Mamba to establish the sequential scanning from lower to higher frequencies, enabling the network to initially reconstruct coarse structures and progressively refine the image with more intricate details. Furthermore, recognizing the variable frequency distributions that arise from DCT transformations across different image sizes, we have developed Scale-Adaptive Normalization to manage these variations adeptly. Comprehensive experiments confirm that DCTMamba significantly outperforms existing solutions, achieving high fidelity in both coarse structures and fine details.CTMamba significantly outperforms existing solutions, achieving high fidelity in both coarse structures and fine details.

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IJCAI Conference 2025 Conference Paper

Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration

Long Peng
Xin Di
ZhanFeng Feng
Wenbo Li
Renjing Pei
Yang Wang
Xueyang Fu
Yang Cao

Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (e. g. , 4K and 8K), achieving a balance between restoration quality and computational efficiency has become increasingly critical. Existing methods, primarily based on CNNs, Transformers, or their hybrid approaches, apply uniform deep representation extraction across the image. However, these methods often struggle to effectively model long-range dependencies and largely overlook the spatial characteristics of image degradation (regions with richer textures tend to suffer more severe damage), making it hard to achieve the best trade-off between restoration quality and efficiency. To address these issues, we propose a novel texture-aware image restoration method, TAMambaIR, which simultaneously perceives image textures and achieves a trade-off between performance and efficiency. Specifically, we introduce a novel Texture-Aware State Space Model, which enhances texture awareness and improves efficiency by modulating the transition matrix of the state-space equation and focusing on regions with complex textures. Additionally, we design a Multi-Directional Perception Block to improve multi-directional receptive fields while maintaining low computational overhead. Extensive experiments on benchmarks for image super-resolution, deraining, and low-light image enhancement demonstrate that TAMambaIR achieves state-of-the-art performance with significantly improved efficiency, establishing it as a robust and efficient framework for image restoration.

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AAAI Conference 2025 Conference Paper

DreamUHD: Frequency Enhanced Variational Autoencoder for Ultra-High-Definition Image Restoration

Yidi Liu
Dong Li
Jie Xiao
Yuanfei Bao
Senyan Xu
Xueyang Fu

Existing ultra-high-definition (UHD) image restoration methods often struggle with consistency due to downsampling. We aim to address these challenges by leveraging the powerful latent space representation and reconstruction capabilities of Variational Autoencoders (VAE). However, applying VAE to UHD image restoration presents challenges: 1) High-performing VAEs have large parameter sizes, leading to significant carbon footprints; 2) The self-reconstruction property of VAE hinders bridging the domain gap between clean and degraded images; 3) Latent encoding in VAE can lose high-frequency information, compromising image detail. To overcome these challenges, we propose a frequency enhanced VAE UHD image restoration framework by integrating frequency priors. First, we design the Fourier-based lightweight frequency learning within the VAE to improve parameter efficiency. Then, we introduce a wavelet-based adapter that extracts multi-scale image information and employs frequency-aware adaptive modulation to bridge the domain gap by integrating degraded image data into the pre-trained VAE. Additionally, the adapter injects high-frequency information into the VAE decoder, enhancing detail in the restored images. In this way, our method effectively combines the powerful latent space representation with frequency priors to enhance UHD image restoration. Extensive experiments on various UHD image restoration tasks show that our method surpasses state-of-the-art methods both qualitatively and quantitatively.

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AAAI Conference 2025 Conference Paper

EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction

Chengjie Ge
Xueyang Fu
Peng He
Kunyu Wang
Chengzhi Cao
Zheng-Jun Zha

Leveraging its robust linear global modeling capability, Mamba has notably excelled in computer vision. Despite its success, existing Mamba-based vision models have overlooked the nuances of event-driven tasks, especially in video reconstruction. Event-based video reconstruction (EBVR) demands spatial translation invariance and close attention to local event relationships in the spatio-temporal domain. Unfortunately, conventional Mamba algorithms apply static window partitions and standard reshape scanning methods, leading to significant losses in local connectivity. To overcome these limitations, we introduce EventMamba—a specialized model designed for EBVR task. EventMamba innovates by incorporating random window offset (RWO) in the spatial domain, moving away from the restrictive fixed partitioning. Additionally, it features a new consistent traversal serialization approach in the spatio-temporal domain, which maintains the proximity of adjacent events both spatially and temporally. These enhancements enable EventMamba to retain Mamba’s robust modeling capabilities while significantly preserving the spatio-temporal locality of event data. Comprehensive testing on multiple datasets shows that EventMamba markedly enhances video reconstruction, drastically improving computation speed while delivering superior visual quality compared to Transformer-based methods.

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ICML Conference 2025 Conference Paper

FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining

Dong Li 0055
Yidi Liu
Xueyang Fu
Jie Huang 0017
Senyan Xu
Qi Zhu 0010
Zheng-Jun Zha

Image deraining aims to remove rain streaks from rainy images and restore clear backgrounds. Currently, some research that employs the Fourier transform has proved to be effective for image deraining, due to it acting as an effective frequency prior for capturing rain streaks. However, despite there exists dependency of low frequency and high frequency in images, these Fourier-based methods rarely exploit the correlation of different frequencies for conjuncting their learning procedures, limiting the full utilization of frequency information for image deraining. Alternatively, the recently emerged Mamba technique depicts its effectiveness and efficiency for modeling correlation in various domains (e. g. , spatial, temporal), and we argue that introducing Mamba into its unexplored Fourier spaces to correlate different frequencies would help improve image deraining. This motivates us to propose a new framework termed FourierMamba, which performs image deraining with Mamba in the Fourier space. Owing to the unique arrangement of frequency orders in Fourier space, the core of FourierMamba lies in the scanning encoding of different frequencies, where the low-high frequency order formats exhibit differently in the spatial dimension (unarranged in axis) and channel dimension (arranged in axis). Therefore, we design FourierMamba that correlates Fourier space information in the spatial and channel dimensions with distinct designs. Specifically, in the spatial dimension Fourier space, we introduce the zigzag coding to scan the frequencies to rearrange the orders from low to high frequencies, thereby orderly correlating the connections between frequencies; in the channel dimension Fourier space with arranged orders of frequencies in axis, we can directly use Mamba to perform frequency correlation and improve the channel information representation. Extensive experiments reveal that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

NeurIPS Conference 2025 Conference Paper

Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement

Yidi Liu
Xueyang Fu
Jie Huang
Jie Xiao
Dong Li
Wenlong Zhang
Lei Bai
Zheng-Jun Zha

Ultra-High Definition (UHD) image restoration struggles to balance computational efficiency and detail retention. While Variational Autoencoders (VAEs) offer improved efficiency by operating in the latent space, with the Gaussian variational constraint, this compression preserves semantics but sacrifices critical high-frequency attributes specific to degradation and thus compromises reconstruction fidelity. % This compromises reconstruction fidelity, even when global semantics are preserved. Consequently, a VAE redesign is imperative to foster a robust semantic representation conducive to generalization and perceptual quality, while simultaneously enabling effective high-frequency information processing crucial for reconstruction fidelity. To address this, we propose \textit{Latent Harmony}, a two-stage framework that reinvigorates VAEs for UHD restoration by concurrently regularizing the latent space and enforcing high-frequency-aware reconstruction constraints. Specifically, Stage One introduces the LH-VAE, which fortifies its latent representation through visual semantic constraints and progressive degradation perturbation for enhanced semantics robustness; meanwhile, it incorporates latent equivariance to bolster its high-frequency reconstruction capabilities. Then, Stage Two facilitates joint training of this refined VAE with a dedicated restoration model. This stage integrates High-Frequency Low-Rank Adaptation (HF-LoRA), featuring two distinct modules: an encoder LoRA, guided by a fidelity-oriented high-frequency alignment loss, tailored for the precise extraction of authentic details from degradation-sensitive high-frequency components; and a decoder LoRA, driven by a perception-oriented loss, designed to synthesize perceptually superior textures. These LoRA modules are meticulously trained via alternating optimization with selective gradient propagation to preserve the integrity of the pre-trained latent structure. This methodology culminates in a flexible fidelity-perception trade-off at inference, managed by an adjustable parameter $\alpha$. Extensive experiments demonstrate that \textit{Latent Harmony} effectively balances perceptual and reconstructive objectives with efficiency, achieving superior restoration performance across diverse UHD and standard-resolution scenarios.

IJCAI Conference 2025 Conference Paper

Learnable Frequency Decomposition for Image Forgery Detection and Localization

Dong Li
Jiayíng Zhu
Yidi Liu
Xin Lu
Xueyang Fu
Jiawei Liu
Aiping Liu
Zheng-Jun Zha

Concern for image authenticity spurs research in image forgery detection and localization (IFDL). Most deep learning-based methods focus primarily on spatial domain modeling and have not fully explored frequency domain strategies. In this paper, we observe and analyze the frequency characteristic changes caused by image tampering. Observations indicate that manipulation traces are especially prominent in phase components and span both low and high-frequency bands. Based on these findings, we propose a forensic frequency decomposition network (F2D-Net), which incorporates deep Fourier transforms and leverages both phase information and high and low-frequency components to enhance IFDL. Specifically, F2D-Net consists of the Spectral Decomposition Subnetwork (SDSN) and the Frequency Separation Subnetwork (FSSN). The former decomposes the image into amplitude and phase, focusing on learning the semantic content in the phase spectrum to identify forged objects, thus improving forgery detection accuracy. The latter further adaptively decomposes the output of the SDSN to obtain corresponding high and low frequencies, and applies a divide-and-conquer strategy to refine each frequency band, mitigating the optimization difficulties caused by coupled forgery traces across different frequencies, thereby better capturing the pixels belonging to the forged object to improve localization accuracy. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art image forgery detection and localization techniques both qualitatively and quantitatively.

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AAAI Conference 2025 Conference Paper

Motion-adaptive Transformer for Event-based Image Deblurring

Senyan Xu
Zhijing Sun
Mingchen Zhong
Chengzhi Cao
Yidi Liu
Xueyang Fu
Yan Chen

Event cameras, which capture pixel-level brightness changes asynchronously, provide rich motion information that is often missed during traditional frame-based camera exposures, thereby offering fresh perspectives for motion deblurring. Although current approaches incorporate event intensity, they neglect essential spatial motion information. Unlike their CNN architectures, Transformers excel in modeling long-range dependencies but struggle with establishing relevant non-local connections in sparse events and fail to highlight significant interactions in dense images. To address these limitations, we introduce a Motion-Adaptive Transformer network (MAT) that utilizes spatial motion information to forge robust global connections. The core design is an Adaptive Motion Mask Predictor (AMMP) that identifies key motion regions, guiding the Motion-Sparse Attention (MSA) to eliminate irrelevant event tokens and enabling the Motion-Aware Attention (MAA) to focus on relevant ones, thereby enhancing long-range dependency modeling. Additionally, we elaborately design a Cross-Modal Intensity Gating mechanism that efficiently merges intensity data across modalities while minimizing parameter use. The learnable Expansion-Controlled Spatial Gating further optimizes the transmission of event features. Comprehensive testing confirms that our approach sets a new benchmark in image deblurring, surpassing previous methods by up to 0.60dB on the GoPro dataset, 1.04dB on the HS-ERGB dataset, and achieving an average improvement of 0.52dB across two real-world datasets.

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NeurIPS Conference 2025 Conference Paper

Neural Fractional Attention Differential Equations

Qiyu Kang
Wenjun Cui
Xuhao Li
Yuxin Ma
Xueyang Fu
Wee Peng Tay
Yidong Li
Zheng-Jun Zha

The integration of differential equations with neural networks has created powerful tools for modeling complex dynamics effectively across diverse machine learning applications. While standard integer-order neural ordinary differential equations (ODEs) have shown considerable success, they are limited in their capacity to model systems with memory effects and historical dependencies. Fractional calculus offers a mathematical framework capable of addressing this limitation, yet most current fractional neural networks use static memory weightings that cannot adapt to input-specific contextual requirements. This paper proposes a generalized neural Fractional Attention Differential Equation (FADE), which combines the memory-retention capabilities of fractional calculus with contextual learnable attention mechanisms. Our approach replaces fixed kernel functions in fractional operators with neural attention kernels that adaptively weight historical states based on their contextual relevance to current predictions. This allows our framework to selectively emphasize important temporal dependencies while filtering less relevant historical information. Our theoretical analysis establishes solution boundedness, problem well-posedness, and numerical equation solver convergence properties of the proposed model. Furthermore, through extensive evaluation on tasks such as fluid flow, graph learning problems and spatio-temporal traffic flow forecasting, we demonstrate that our adaptive attention-based fractional framework outperforms both integer-order neural ODE models and existing fractional approaches. The results confirm that our framework provides superior modeling capacity for complex dynamics with varying temporal dependencies. The code is available at \url{https: //github. com/cuiwjTech/NeurIPS2025_FADE}.

NeurIPS Conference 2025 Conference Paper

PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation

Kunyu Wang
Xueyang Fu
Yuanfei Bao
Chengjie Ge
Chengzhi Cao
Wei Zhai
Zheng-Jun Zha

Continual Test-Time Adaptation (CTTA) aims to online adapt a pre-trained model to changing environments during inference. Most existing methods focus on exploiting target data, while overlooking another crucial source of information, the pre-trained weights, which encode underutilized domain-invariant priors. This paper takes the geometric attributes of pre-trained weights as a starting point, systematically analyzing three key components: magnitude, absolute angle, and pairwise angular structure. We find that the pairwise angular structure remains stable across diverse corrupted domains and encodes domain-invariant semantic information, suggesting it should be preserved during adaptation. Based on this insight, we propose PAID (Pairwise Angular Invariant Decomposition), a prior-driven CTTA method that decomposes weight into magnitude and direction, and introduces a learnable orthogonal matrix via Householder reflections to globally rotate direction while preserving the pairwise angular structure. During adaptation, only the magnitudes and the orthogonal matrices are updated. PAID achieves consistent improvements over recent SOTA methods on four widely used CTTA benchmarks, demonstrating that preserving pairwise angular structure offers a simple yet effective principle for CTTA. Our code is available at https: //github. com/wangkunyu241/PAID.

IJCAI Conference 2025 Conference Paper

PanComplex: Leveraging Complex-Valued Neural Networks for Enhanced Pansharpening

Chunhui Luo
Dong Li
Xiaoliang Ma
Xin Lu
Zhiyuan Wang
Jiangtong Tan
Xueyang Fu

Pansharpening combines panchromatic and low-resolution multispectral images to generate high-resolution multispectral images. Previous studies have explored the connection between pansharpening and the frequency domain, but mostly in the real-valued domain, leaving the complex domain relatively unexplored. To redefine the pansharpening task, we propose a complex-valued spatial-frequency dual-domain framework, PanComplex. To achieve this, we first establish complex representations and introduce basic complex operators tailored to pansharpening, enabling the transformation of multispectral real-valued signals into the complex domain for learning. We then model both spatial and frequency branches to capture global frequency features and local spatial features comprehensively. Finally, we employ a complex-based interaction module to fuse the spatial and frequency features, achieving complementary information across both domains. By using the representation power of the complex domain, PanComplex effectively extracts complementary features from PAN and MS images, thereby enhancing pansharpening performance. Experiments on multiple datasets demonstrate that our method achieves optimal performance with the fewest parameters and exhibits strong generalization ability to other tasks. The source code for this work is publicly available at https: //github. com/lch-ustc/PanComplex.

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AAAI Conference 2025 Conference Paper

SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation

Hongjian Liu
Qingsong Xie
Tianxiang Ye
Zhijie Deng
Chen Chen
Shixiang Tang
Xueyang Fu
Haonan Lu

The iterative sampling procedure employed by diffusion models (DMs) often leads to significant latency. To address this, we propose Stochastic Consistency Distillation (SCott) to enable accelerated text-to-image generation, where high-quality generations can be achieved with just 2-4 sampling steps or even1 step, and further improvements can be obtained by additional cost, e.g., 4 steps. In contrast to vanilla consistency distillation (CD) which distills the ordinary differential equation solvers-based sampling process of a pre-trained teacher model into a student, SCott explores the possibility and validates the efficacy of integrating stochastic differential equation (SDE) solvers into CD to fully unleash the potential of the teacher. SCott is augmented with elaborate strategies to control the noise strength and sampling process of the SDE solver. An adversarial loss is further incorporated to strengthen the sample quality with rare sampling steps. Empirically, on the MSCOCO-2017 5K dataset with a Stable Diffusion-V1.5 teacher, SCott achieves an FID of 21.9, surpassing that of the 1-step InstaFlow (23.4) and the 4-step UFOGen (22.1). Moreover, SCott can yield more diverse samples than other consistency models for high-resolution image generation, with up to 16% improvement in a qualified metric.

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ICML Conference 2024 Conference Paper

CCM: Real-Time Controllable Visual Content Creation Using Text-to-Image Consistency Models

Jie Xiao 0002
Kai Zhu 0004
Han Zhang 0010
Zhiheng Liu
Yujun Shen
Zhantao Yang
Ruili Feng
Yu Liu 0063

Consistency Models (CMs) have showed a promise in creating high-quality images with few steps. However, the way to add new conditional controls to the pre-trained CMs has not been explored. In this paper, we explore the pivotal subject of leveraging the generative capacity and efficiency of consistency models to facilitate controllable visual content creation via ControlNet. First, it is observed that ControlNet trained for diffusion models (DMs) can be directly applied to CMs for high-level semantic controls but sacrifice image low-level details and realism. To tackle with this issue, we develop a CMs-tailored training strategy for ControlNet using the consistency training. It is substantiated that ControlNet can be successfully established through the consistency training technique. Besides, a unified adapter can be trained utilizing the consistency training, which enhances the adaptation of DM’s ControlNet. We quantitatively and qualitatively evaluate all strategies across various conditional controls, including sketch, hed, canny, depth, human pose, low-resolution image and masked image, with the pre-trained text-to-image latent consistency models.

ICLR Conference 2024 Conference Paper

DreamClean: Restoring Clean Image Using Deep Diffusion Prior

Jie Xiao 0002
Ruili Feng
Han Zhang 0010
Zhiheng Liu
Zhantao Yang
Yurui Zhu
Xueyang Fu
Kai Zhu 0004

Image restoration poses a garners substantial interest due to the exponential surge in demands for recovering high-quality images from diverse mobile camera devices, adverse lighting conditions, suboptimal shooting environments, and frequent image compression for efficient transmission purposes. Yet this problem gathers significant challenges as people are blind to the type of restoration the images suffer, which, is usually the case in real-day scenarios and is most urgent to solve for this field. Current research, however, heavily relies on prior knowledge of the restoration type, either explicitly through rules or implicitly through the availability of degraded-clean image pairs to define the restoration process, and consumes considerable effort to collect image pairs of vast degradation types. This paper introduces DreamClean, a training-free method that needs no degradation prior knowledge but yields high-fidelity and generality towards various types of image degradation. DreamClean embeds the degraded image back to the latent of pre-trained diffusion models and re-sample it through a carefully designed diffusion process that mimics those generating clean images. Thanks to the rich image prior in diffusion models and our novel Variance Preservation Sampling (VPS) technique, DreamClean manages to handle various different degradation types at one time and reaches far more satisfied final quality than previous competitors. DreamClean relies on elegant theoretical supports to assure its convergence to clean image when VPS has appropriate parameters, and also enjoys superior experimental performance over various challenging tasks that could be overwhelming for previous methods when degradation prior is unavailable.

AAAI Conference 2024 Conference Paper

Learning Discriminative Noise Guidance for Image Forgery Detection and Localization

Jiaying Zhu
Dong Li
Xueyang Fu
Gang Yang
Jie Huang
Aiping Liu
Zheng-Jun Zha

This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tampering technology complicates the direct application of noise for forgery detection, as the noise inconsistency between forged and authentic regions is not fully exploited. To tackle this, we develop a two-step discriminative noise-guided approach to explicitly enhance the representation and use of noise inconsistencies, thereby fully exploiting noise information to improve the accuracy and robustness of forgery detection. Specifically, we first enhance the noise discriminability of forged regions compared to authentic ones using a de-noising network and a statistics-based constraint. Then, we merge a model-driven guided filtering mechanism with a data-driven attention mechanism to create a learnable and differentiable noise-guided filter. This sophisticated filter allows us to maintain the edges of forged regions learned from the noise. Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

Neuromorphic Event Signal-Driven Network for Video De-raining

Chengjie Ge
Xueyang Fu
Peng He
Kunyu Wang
Chengzhi Cao
Zheng-Jun Zha

Convolutional neural networks-based video de-raining methods commonly rely on dense intensity frames captured by CMOS sensors. However, the limited temporal resolution of these sensors hinders the capture of dynamic rainfall information, limiting further improvement in de-raining performance. This study aims to overcome this issue by incorporating the neuromorphic event signal into the video de-raining to enhance the dynamic information perception. Specifically, we first utilize the dynamic information from the event signal as prior knowledge, and integrate it into existing de-raining objectives to better constrain the solution space. We then design an optimization algorithm to solve the objective, and construct a de-raining network with CNNs as the backbone architecture using a modular strategy to mimic the optimization process. To further explore the temporal correlation of the event signal, we incorporate a spiking self-attention module into our network. By leveraging the low latency and high temporal resolution of the event signal, along with the spatial and temporal representation capabilities of convolutional and spiking neural networks, our model captures more accurate dynamic information and significantly improves de-raining performance. For example, our network achieves a 1.24dB improvement on the SynHeavy25 dataset compared to the previous state-of-the-art method, while utilizing only 39% of the parameters.

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IJCAI Conference 2023 Conference Paper

Accurate MRI Reconstruction via Multi-Domain Recurrent Networks

Jinbao Wei
Zhijie Wang
Kongqiao Wang
Li Guo
Xueyang Fu
Ji Liu
Xun Chen

In recent years, deep convolutional neural networks (CNNs) have become dominant in MRI reconstruction from undersampled k-space. However, most existing CNNs methods reconstruct the undersampled images either in the spatial domain or in the frequency domain, and neglecting the correlation between these two domains. This hinders the further reconstruction performance improvement. To tackle this issue, in this work, we propose a new multi-domain recurrent network (MDR-Net) with multi-domain learning (MDL) blocks as its basic units to reconstruct the undersampled MR image progressively. Specifically, the MDL block interactively processes the local spatial features and the global frequency information to facilitate complementary learning, leading to fine-grained features generation. Furthermore, we introduce an effective frequency-based loss to narrow the frequency spectrum gap, compensating for over-smoothness caused by the widely used spatial reconstruction loss. Extensive experiments on public fastMRI datasets demonstrate that our MDR-Net consistently outperforms other competitive methods and is able to provide more details.

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ICML Conference 2023 Conference Paper

Random Shuffle Transformer for Image Restoration

Jie Xiao 0002
Xueyang Fu
Man Zhou 0003
Hongjian Liu
Zheng-Jun Zha

Non-local interactions play a vital role in boosting performance for image restoration. However, local window Transformer has been preferred due to its efficiency for processing high-resolution images. The superiority in efficiency comes at the cost of sacrificing the ability to model non-local interactions. In this paper, we present that local window Transformer can also function as modeling non-local interactions. The counterintuitive function is based on the permutation-equivariance of self-attention. The basic principle is quite simple: by randomly shuffling the input, local self-attention also has the potential to model non-local interactions without introducing extra parameters. Our random shuffle strategy enjoys elegant theoretical guarantees in extending the local scope. The resulting Transformer dubbed ShuffleFormer is capable of processing high-resolution images efficiently while modeling non-local interactions. Extensive experiments demonstrate the effectiveness of ShuffleFormer across a variety of image restoration tasks, including image denoising, deraining, and deblurring. Code is available at https: //github. com/jiexiaou/ShuffleFormer.

JBHI Journal 2022 Journal Article

A Joint Constrained CCA Model for Network-Dependent Brain Subregion Parcellation

Qinrui Ling
Aiping Liu
Yu Li
Xueyang Fu
Xun Chen
Martin J. McKeown
Feng Wu

Connectivity-based brain region parcellation from functional magnetic resonance imaging (fMRI) data is complicated by heterogeneity among aged and diseased subjects, particularly when the data are spatially transformed to a common space. Here, we propose a group-guided functional brain region parcellation model capable of obtaining subregions from a target region with consistent connectivity profiles across multiple subjects, even when the fMRI signals are kept in their native spaces. The model is based on a joint constrained canonical correlation analysis (JC-CCA) method that achieves group-guided parcellation while allowing the data dimension of the parcellated regions for each subject to vary. We performed extensive experiments on synthetic and real data to demonstrate the superiority of the proposed model compared to other classical methods. When applied to fMRI data of subjects with and without Parkinson's disease (PD) to estimate the subregions in the Putamen, significant between-group differences were found in the derived subregions and the connectivity patterns. Superior classification and regression results were obtained, demonstrating its potential in clinical practice.

AAAI Conference 2022 Conference Paper

Efficient Model-Driven Network for Shadow Removal

Yurui Zhu
Zeyu Xiao
Yanchi Fang
Xueyang Fu
Zhiwei Xiong
Zheng-Jun Zha

Deep Convolutional Neural Networks (CNNs) based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially variant property of the shadow images, hindering their further performance. Second, most deep CNNs based methods directly estimate the shadow free results from the input shadow images like a black box, thus losing the desired interpretability. To address these issues, we first propose a new shadow illumination model for the shadow removal task. This new shadow illumination model ensures the identity mapping among unshaded regions, and adaptively performs fine grained spatial mapping between shadow regions and their references. Then, based on the shadow illumination model, we reformulate the shadow removal task as a variational optimization problem. To effectively solve the variational problem, we design an iterative algorithm and unfold it into a deep network, naturally increasing the interpretability of the deep model. Experiments show that our method could achieve SOTA performance with less than half parameters, one-fifth of floating-point of operations (FLOPs), and over seventeen times faster than SOTA method (DHAN).

IJCAI Conference 2022 Conference Paper

Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network

Chengzhi Cao
Xueyang Fu
Yurui Zhu
Gege Shi
Zheng-Jun Zha

Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial brightness changes nor the temporal correlation between events at each time step, resulting in insufficient use of spatio-temporal information. To address this issue, we propose a new Spatio-Temporal Relation-Attention network (STRA), for the specific event-based video deblurring. Concretely, to utilize spatial consistency between the frame and event, we model the brightness changes as an extra prior to aware blurring contexts in each frame; to record temporal relationship among different events, we develop a temporal memory block to restore long-range dependencies of event sequences continuously. In this way, the complementary information contained in the events and frames, as well as the correlation of neighboring events, can be fully utilized to recover spatial texture from events constantly. Experiments show that our STRA significantly outperforms several competing methods, e. g. , on the HQF dataset, our network achieves up to 1. 3 dB in terms of PSNR over the most advanced method. The code is available at https: //github. com/Chengzhi-Cao/STRA.

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IJCAI Conference 2022 Conference Paper

Exploring Fourier Prior for Single Image Rain Removal

Xin Guo
Xueyang Fu
Man Zhou
Zhen Huang
Jialun Peng
Zheng-Jun Zha

Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To address this issue, we propose a simple but effective prior - Fourier prior to improve the generalization ability of an image rain removal model. The Fourier prior is a kind of property of rainy images. It is based on a key observation of us - replacing the Fourier amplitude of rainy images with that of clean images greatly suppresses the synthetic and real-world rain streaks. This means the amplitude contains most of the rain streak information and the phase keeps the similar structures of the background. So it is natural for single image rain removal to process the amplitude and phase information of the rainy images separately. In this paper, we develop a two-stage model where the first stage restores the amplitude of rainy images to clean rain streaks, and the second stage restores the phase information to refine fine-grained background structures. Extensive experiments on synthetic rainy data demonstrate the power of Fourier prior. Moreover, when trained on synthetic data, a robust generalization ability to real-world images can also be obtained. The code will be publicly available at https: //github. com/willinglucky/ExploringFourier-Prior-for-Single-Image-Rain-Removal.

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AAAI Conference 2022 Conference Paper

Learning to Model Pixel-Embedded Affinity for Homogeneous Instance Segmentation

Wei Huang
Shiyu Deng
Chang Chen
Xueyang Fu
Zhiwei Xiong

Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into different instances, have received increasing attention due to their efficient pipeline. However, they often fail to distinguish adjacent instances due to similar appearances, dense distribution and ambiguous boundaries of instances in homogeneous images. In this paper, we propose a pixel-embedded affinity modeling method for homogeneous instance segmentation, which is able to preserve the semantic information of instances and improve the distinguishability of adjacent instances. Instead of predicting affinity directly, we propose a self-correlation module to explicitly model the pairwise relationships between pixels, by estimating the similarity between embeddings generated from the input image through CNNs. Based on the self-correlation module, we further design a cross-correlation module to maintain the semantic consistency between instances. Specifically, we map the transformed input images with different views and appearances into the same embedding space, and then mutually estimate the pairwise relationships of embeddings generated from the original input and its transformed variants. In addition, to integrate the global instance information, we introduce an embedding pyramid module to model affinity on different scales. Extensive experiments demonstrate the versatile and superior performance of our method on three representative datasets. Code and models are available at https: //github. com/weih527/ Pixel-Embedded-Affinity.

AAAI Conference 2022 Conference Paper

RETRACTEPan-Sharpening with Customized Transformer and Invertible Neural Network

Man Zhou
Jie Huang
Yanchi Fang
Xueyang Fu
Aiping Liu

In remote sensing imaging systems, pan-sharpening is an important technique to obtain high-resolution multispectral images from a high-resolution panchromatic image and its corresponding low-resolution multispectral image. Owing to the powerful learning capability of convolution neural network (CNN), CNN-based methods have dominated this field. However, due to the limitation of the convolution operator, long-range spatial features are often not accurately obtained, thus limiting the overall performance. To this end, we propose a novel and effective method by exploiting a customized transformer architecture and information-lossless invertible neural module for long-range dependencies modeling and effective feature fusion in this paper. Specifically, the customized transformer formulates the PAN and MS features as queries and keys to encourage joint feature learning across two modalities while the designed invertible neural module enables effective feature fusion to generate the expected pan-sharpened results. To the best of our knowledge, this is the first attempt to introduce transformer and invertible neural network into pan-sharpening field. Extensive experiments over different kinds of satellite datasets demonstrate that our method outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops. Further, the ablation experiments also prove the effectiveness of the proposed customized long-range transformer and effective invertible neural feature fusion module for pan-sharpening. Editorial Notes This article, which was published in Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), has been retracted by agreement between the authors and the journal.

NeurIPS Conference 2022 Conference Paper

Stochastic Window Transformer for Image Restoration

Jie Xiao
Xueyang Fu
Feng Wu
Zheng-Jun Zha

Thanks to the powerful representation capabilities, transformers have made impressive progress in image restoration. However, existing transformers-based methods do not carefully consider the particularities of image restoration. In general, image restoration requires that an ideal approach should be translation-invariant to the degradation, i. e. , the undesirable degradation should be removed irrespective of its position within the image. Furthermore, the local relationships also play a vital role, which should be faithfully exploited for recovering clean images. Nevertheless, most transformers either adopt local attention with the fixed local window strategy or global attention, which unfortunately breaks the translation invariance and causes huge loss of local relationships. To address these issues, we propose an elegant stochastic window strategy for transformers. Specifically, we first introduce the window partition with stochastic shift to replace the original fixed window partition for training. Then, we design a new layer expectation propagation algorithm to efficiently approximate the expectation of the induced stochastic transformer for testing. Our stochastic window transformer not only enjoys powerful representation but also maintains the desired property of translation invariance and locality. Experiments validate the stochastic window strategy consistently improves performance on various image restoration tasks (deraining, denoising and deblurring) by significant margins. The code is available at https: //github. com/jiexiaou/Stoformer.

AAAI Conference 2021 Conference Paper

Rain Streak Removal via Dual Graph Convolutional Network

Xueyang Fu
Qi Qi
Zheng-Jun Zha
Yurui Zhu
Xinghao Ding

Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is rarely considered for this specific task. To address the above problem, we propose a simple yet effective dual graph convolutional network (GCN) for single image rain removal. Specifically, we design two graphs to perform global relational modeling and reasoning. The first GC- N is used to explore global spatial relations among pixels in feature maps, while the second GCN models the global relations across the channels. Compared to standard convolutional operations, the proposed two graphs enable the network to extract representations from new dimensions. To achieve the image rain removal, we further embed these two graphs and multi-scale dilated convolution into a symmetrically skip-connected network architecture. Therefore, our dual graph convolutional network is able to well handle complex and spatially long rain streaks by exploring multiple representations, e. g. , multi-scale local feature, global spatial coherence and cross-channel correlation. Meanwhile, our model is easy to implement, end-to-end trainable and computationally efficient. Extensive experiments on synthetic and real data demonstrate that our method achieves significant improvements over the recent state-of-the-art methods.

NeurIPS Conference 2021 Conference Paper

Unfolding Taylor's Approximations for Image Restoration

Man Zhou
Xueyang Fu
Zeyu Xiao
Gang Yang
Aiping Liu
Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping networks without deepening into the rationality, and neglect the intrinsic prior knowledge of restoration task. To solve the above problems, inspired by Taylor’s Approximations, we unfold Taylor’s Formula to construct a novel framework for image restoration. We find the main part and the derivative part of Taylor’s Approximations take the same effect as the two competing goals of high-level contextualized information and spatial details of image restoration respectively. Specifically, our framework consists of two steps, which are correspondingly responsible for the mapping and derivative functions. The former first learns the high-level contextualized information and the later combines it with the degraded input to progressively recover local high-order spatial details. Our proposed framework is orthogonal to existing methods and thus can be easily integrated with them for further improvement, and extensive experiments demonstrate the effectiveness and scalability of our proposed framework.

IJCAI Conference 2020 Conference Paper

JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks

Menglu Wang
Xueyang Fu
Zepei Sun
Zheng-Jun Zha

Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly utilized to train deep networks. To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal. We first design a quality ranker to measure the compression degree, which is highly correlated with the JPEG quality. Based on this differentiable ranker, we then propose one quality-related loss and one feature matching loss to guide de-blocking and perceptual quality optimization. In addition, we utilize dilated convolutions to extract multi-scale features, which enables our single model to handle multiple compression quality factors. Our method can implicitly use the information contained in the compression factors to produce better results. Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements.

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