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Hui Ji

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12 papers

1 author row

AAAI Conference 2026 Conference Paper

Annealed Relaxation of Speculative Decoding for Faster Autoregressive Image Generation

Xingyao Li
Fengzhuo Zhang
Cunxiao Du
Hui Ji

Despite significant progress in auto-regressive image generation, inference remains slow due to the sequential nature of AR models and the ambiguity of image tokens, even when using speculative decoding. Recent works attempt to address this with relaxed speculative decoding but lack theoretical grounding. In this paper, we establish the theoretical basis of relaxed SD and propose COOL-SD, an annealed relaxation of speculative decoding built on two key insights. The first analyzes the total variation (TV) distance between the target model and relaxed speculative decoding and yields an optimal resampling distribution that minimizes an upper bound of the distance. The second uses perturbation analysis to reveal an annealing behaviour in relaxed speculative decoding, motivating our annealed design. Together, these insights enable COOL-SD to generate images faster with comparable quality, or achieve better quality at similar latency. Experiments validate the effectiveness of COOL-SD, showing consistent improvements over prior methods in speed-quality trade-offs.

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YNIMG Journal 2025 Journal Article

Low-intensity transcranial ultrasound stimulation promotes the extinction of fear memory through the BDNF-TrkB signaling pathway

Degong Meng
Cong Zhang
Jiamin Pei
Xiao Zhang
Hanna Lu
Hui Ji
Xiangjian Zhang
Yi Yuan

Synaptic plasticity plays a crucial role in the extinction of fearful memories. Low-intensity transcranial ultrasound stimulation (TUS) can modulate synaptic plasticity and promote the extinction of fear memories. However, the mechanism by which TUS promotes the extinction of fear memory remains unclear. This study aimed to explore whether and how synaptic plasticity under TUS is involved in modulating fear memory and the role of the brain-derived neurotrophic factor (BDNF)-the tropomyosin-related kinase B (TrkB) signaling pathway in this process. We used behavioral tests and two-photon fluorescence imaging to investigate the modulatory effects of TUS on fear memory and examined the formation/elimination of dendritic spines and the calcium activity of pyramidal neurons in the prefrontal cortex in mice in vivo. We found that TUS of the prefrontal cortex can promote fear memory extinction in mice while promoting dendritic spine formation, reducing dendritic spine elimination, increasing pyramidal neuron activity, and enhancing the expression of BDNF and its receptor TrkB. Conversely, inhibiting the BDNF-TrkB signaling pathway weakened these effects of ultrasound stimulation. Our study demonstrated that TUS could promote the extinction of fear memories, indicating that TUS has the potential to be used in the clinical treatment of patients with fear memory.

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AAAI Conference 2025 Conference Paper

Multi-Focus Image Fusion via Explicit Defocus Blur Modelling

Yuhui Quan
Xi Wan
Zitao Tang
Jinxiu Liang
Hui Ji

Multi-focus image fusion (MFIF) enhances depth of field in photography by generating an all-in-focus image from multiple images captured at different focal lengths. While deep learning has shown promise in MFIF, most existing methods overlooked the physical properties of defocus blurring in their network design, limiting their interoperability and generalization. This paper introduces a novel framework that integrates explicit defocus blur modelling into the MFIF process, improving both interpretability and performance. Using an atom-based spatially-varying parameterized defocus blurring model, our approach calculates pixel-wise defocus descriptors and initial focused images from multi-focus source images in a scale-recurrent manner to estimate soft decision maps. Fusion is then performed using masks derived from these decision maps, with special treatment for pixels likely defocused in all source images or near boundaries of defocused/focused regions. The model is trained with a fusion loss and a cross-scale defocus estimation loss. Extensive experiments on benchmark datasets demonstrated the effectiveness of our approach.

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NeurIPS Conference 2025 Conference Paper

Zero-Shot Blind-Spot Image Denoising via Cross-Scale Non-Local Pixel Refilling

Qilong Guo
Tianjing Zhang
Zhiyuan Ma
Hui Ji

Blind-spot denoising (BSD) method is a powerful paradigm for zero-shot image denoising by training models to predict masked target pixels from their neighbors. However, they struggle with real-world noise exhibiting strong local correlations, where efforts to suppress noise correlation often weaken pixel-value dependencies, adversely affecting denoising performance. This paper presents a theoretical analysis quantifying the impact of replacing masked pixels with observations exhibiting weaker noise correlation but potentially reduced similarity, revealing a trade-off that impacts the statistical risk of the estimation. Guided by this insight, we propose a computational scheme that replaces masked pixels with distant ones of similar appearance and lower noise correlation. This strategy improves the prediction by balancing noise suppression and structural consistency. Experiments confirm the effectiveness of our method, outperforming existing zero-shot BSD methods.

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NeurIPS Conference 2024 Conference Paper

Cross-Scale Self-Supervised Blind Image Deblurring via Implicit Neural Representation

Tianjing Zhang
Yuhui Quan
Hui Ji

Blind image deblurring (BID) is an important yet challenging image recovery problem. Most existing deep learning methods require supervised training with ground truth (GT) images. This paper introduces a self-supervised method for BID that does not require GT images. The key challenge is to regularize the training to prevent over-fitting due to the absence of GT images. By leveraging an exact relationship among the blurred image, latent image, and blur kernel across consecutive scales, we propose an effective cross-scale consistency loss. This is implemented by representing the image and kernel with implicit neural representations (INRs), whose resolution-free property enables consistent yet efficient computation for network training across multiple scales. Combined with a progressively coarse-to-fine training scheme, the proposed method significantly outperforms existing self-supervised methods in extensive experiments.

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NeurIPS Conference 2024 Conference Paper

Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising

Yuhui Quan
Tianxiang Zheng
Hui Ji

Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half-plane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impactsfrom the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image-free methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges.

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YNIMG Journal 2023 Journal Article

Low-intensity ultrasound stimulation modulates time-frequency patterns of cerebral blood oxygenation and neurovascular coupling of mouse under peripheral sensory stimulation state

Yi Yuan
Qianqian Wu
Xingran Wang
Mengyang Liu
Jiaqing Yan
Hui Ji

Previous studies have demonstrated that transcranial ultrasound stimulation (TUS) not only modulates cerebral hemodynamics, neural activity, and neurovascular coupling characteristics in resting samples but also exerts a significant inhibitory effect on the neural activity in task samples. However, the effect of TUS on cerebral blood oxygenation and neurovascular coupling in task samples remains to be elucidated. To answer this question, we first used forepaw electrical stimulation of the mice to elicit the corresponding cortical excitation, and then stimulated this cortical region using different modes of TUS, and simultaneously recorded the local field potential using electrophysiological acquisition and hemodynamics using optical intrinsic signal imaging. The results indicate that for the mice under peripheral sensory stimulation state, TUS with a duty cycle of 50% can (1) enhance the amplitude of cerebral blood oxygenation signal, (2) reduce the time-frequency characteristics of evoked potential, (3) reduce the strength of neurovascular coupling in time domain, (4) enhance the strength of neurovascular coupling in frequency domain, and (5) reduce the time-frequency cross-coupling of neurovasculature. The results of this study indicate that TUS can modulate the cerebral blood oxygenation and neurovascular coupling in peripheral sensory stimulation state mice under specific parameters. This study opens up a new area of investigation for potential applicability of TUS in brain diseases related to cerebral blood oxygenation and neurovascular coupling.

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YNIMG Journal 2023 Journal Article

Modulation effects of low-intensity transcranial ultrasound stimulation on the neuronal firing activity and synaptic plasticity of mice

Zhe Zhao
Hui Ji
Cong Zhang
Jiamin Pei
Xiangjian Zhang
Yi Yuan

Low-intensity transcranial ultrasound stimulation (TUS) has been effective in modulating several neurological and psychiatric disorders. However, how TUS modulates neuronal firing activity and synaptic plasticity remains unclear. Thus, we behaviorally tested the whisker-dependent novel object discrimination ability in mice after ultrasound stimulation and examined the cortical neuronal firing activity and synaptic plasticity in awake mice after ultrasound stimulation by two-photon fluorescence imaging. The current study presented the following results: (1) TUS could significantly improve the whisker-dependent new object discrimination ability of mice, suggesting that their learning and memory abilities were significantly enhanced; (2) TUS significantly enhanced neuronal firing activity; and (3) TUS increased the growth rate of dendritic spines in the barrel cortex, but did not promote the extinction of dendritic spines, resulting in enhanced synaptic plasticity. The above results indicate that TUS can improve the learning and memory ability of mice and enhance the neuronal firing activity and synaptic plasticity that are closely related to it. This study provides a research basis for the application of ultrasound stimulation in the treatment of learning- and memory-related diseases.

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AAAI Conference 2023 Conference Paper

Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation

Yuhui Quan
Zhile Chen
Tongyao Pang
Hui Ji

Phase retrieval (PR) is a challenging nonlinear inverse problem in scientific imaging that involves reconstructing the phase of a signal from its intensity measurements. Recently, there has been an increasing interest in deep learning-based PR. Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes a fully-unsupervised learning approach for PR, which trains an end-to-end deep model via a GT-free teacher-student online distillation framework. Specifically, a teacher model is trained using a self-expressive loss with noise resistance, while a student model is trained with a consistency loss on augmented data to exploit the teacher's dark knowledge. Additionally, we develop an enhanced unfolding network for both the teacher and student models. Extensive experiments show that our proposed approach outperforms existing unsupervised PR methods with higher computational efficiency and performs competitively against supervised methods.

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AAAI Conference 2023 Conference Paper

Unsupervised Deep Video Denoising with Untrained Network

Huan Zheng
Tongyao Pang
Hui Ji

Deep learning has become a prominent tool for video denoising. However, most existing deep video denoising methods require supervised training using noise-free videos. Collecting noise-free videos can be costly and challenging in many applications. Therefore, this paper aims to develop an unsupervised deep learning method for video denoising that only uses a single test noisy video for training. To achieve this, an unsupervised loss function is presented that provides an unbiased estimator of its supervised counterpart defined on noise-free video. Additionally, a temporal attention mechanism is proposed to exploit redundancy among frames. The experiments on video denoising demonstrate that the proposed unsupervised method outperforms existing unsupervised methods and remains competitive against recent supervised deep learning methods.

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NeurIPS Conference 2021 Conference Paper

Gaussian Kernel Mixture Network for Single Image Defocus Deblurring

Yuhui Quan
Zicong Wu
Hui Ji

Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount. This paper presents an end-to-end deep learning approach for removing defocus blur from a single image, so as to have an all-in-focus image for consequent vision tasks. First, a pixel-wise Gaussian kernel mixture (GKM) model is proposed for representing spatially variant defocus blur kernels in an efficient linear parametric form, with higher accuracy than existing models. Then, a deep neural network called GKMNet is developed by unrolling a fixed-point iteration of the GKM-based deblurring. The GKMNet is built on a lightweight scale-recurrent architecture, with a scale-recurrent attention module for estimating the mixing coefficients in GKM for defocus deblurring. Extensive experiments show that the GKMNet not only noticeably outperforms existing defocus deblurring methods, but also has its advantages in terms of model complexity and computational efficiency.

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YNICL Journal 2019 Journal Article

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

Chong-Yaw Wee
Chaoqiang Liu
Annie Lee
Joann S. Poh
Hui Ji
Anqi Qiu

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T1-weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n = 3602) and an Asian cohort (n = 347). For the ADNI-2 cohort, the graph-CNN showed classification accuracy of controls (CN) vs. AD at 85. 8% and early MCI (EMCI) vs. AD at 79. 2%, followed by CN vs. late MCI (LMCI) (69. 3%), LMCI vs. AD (65. 2%), EMCI vs. LMCI (60. 9%), and CN vs. EMCI (51. 8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T1-weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The find-tuned graph-CNN further provided a promising CN vs. AD classification accuracy of 89. 4% on the ADNI-1 cohort and >90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classifiers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classification performance.

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