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Haijun Liu

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5 papers

1 author row

EAAI Journal 2026 Journal Article

Multiscale wavelet-based spatial–spectral compression network for hyperspectral image

Hang Yu
Mingyang Wan
Tao Chen
Aibin Peng
Xiangfei Shen
Rulong He
Lihui Chen
Haijun Liu

Hyperspectral images (HSIs) possess high-dimensional tensor structures that present significant reconstruction challenges under ultra-low compression ratios (CR) in artificial intelligence-driven remote sensing. Conventional compression methods are unable to effectively capture inherent spatial–spectral coherence and often neglect multiscale spectral absorption-reflection dependencies, which are critical for maintaining spectral fidelity. To overcome these shortcomings, we propose a Multiscale Wavelet-based Spatial-Spectral Compression Network (MWC-Net) for HSI reconstruction. Methodologically, MWC-Net integrates a three-dimensional (3D) spatial–spectral attention encoder, which via tri-branch attention to extract complete spatial–spectral coherence. Additionally, we develop a multiscale wavelet spatial–spectral decoder that restores scale-sensitive spectral features through multiscale super-resolution and enhances spatial–spectral resolution using wavelet decomposition. Compared to the state-of-the-art method “Hyperspectral Image Compression Sensing Network With Convolutional Neural Networks (CNN)–Transformer Mixture Architectures”, MWC-Net achieves an average decrease from 1. 770 to 1. 549 in the spectral angle mapper (SAM) metric. Additionally, the average peak signal-to-noise ratio (PSNR) increases from 39. 81 to 40. 79, while the average root mean square error (RMSE) decreases from 55. 15 to 49. 97, under approximately 1% CR. This enhancement highlights the superior ability of MWC-Net to balance compression efficiency and spectral fidelity in HSI reconstruction. The code can be available on https: //github. com/YuHang-max/MWCNet.

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AAAI Conference 2025 Conference Paper

EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution

Xi Su
Xiangfei Shen
Mingyang Wan
Jing Nie
Lihui Chen
Haijun Liu
Xichuan Zhou

Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images. In recent years, research on RGB SR has shown that models pre-trained on large-scale benchmark datasets can greatly improve performance on unseen data, which may stand as a remedy for HSI. But how can we transfer the pre-trained RGB model to HSI, to overcome the data-scarcity bottleneck? Because of the significant difference in the channels between the pre-trained RGB model and the HSI, the model cannot focus on the correlation along the spectral dimension, thus limiting its ability to utilize on HSI. Inspired by the HSI spatial-spectral decoupling, we propose a new framework that first fine-tunes the pre-trained model with the spatial components (known as eigenimages), and then infers on unseen HSI using an iterative spectral regularization (ISR) to maintain the spectral correlation. The advantages of our method lie in: 1) we effectively inject the spatial texture processing capabilities of the pre-trained RGB model into HSI while keeping spectral fidelity, 2) learning in the spectral-decorrelated domain can improve the generalizability to spectral-agnostic data, and 3) our inference in the eigenimage domain naturally exploits the spectral low-rank property of HSI, thereby reducing the complexity. This work bridges the gap between pre-trained RGB models and HSI via eigenimages, addressing the issue of limited HSI training data, hence the name EigenSR. Extensive experiments show that EigenSR outperforms the state-of-the-art (SOTA) methods in both spatial and spectral metrics.

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EAAI Journal 2025 Journal Article

Hybrid cross-modality fusion network for medical image segmentation with contrastive learning

Xichuan Zhou
Qianqian Song
Jing Nie
Yujie Feng
Haijun Liu
Fu Liang
Lihui Chen
Jin Xie

Medical image segmentation has been widely adopted in artificial intelligence-based clinical applications. The integration of medical texts into image segmentation models has significantly improved the segmentation performance. It is crucial to design an effective fusion manner to integrate the paired image and text features. Existing multi-modal medical image segmentation methods fuse the paired image and text features through a non-local attention mechanism, which lacks local interaction. Besides, they lack a mechanism to enhance the relevance of the paired features and keep the discriminability of unpaired features in the training process, which limits the segmentation performance. To solve the above problem, we propose a hybrid cross-modality fusion network (HCFNet) based on contrastive learning for medical image segmentation. The key designs of our proposed method are a multi-stage cross-modality contrastive loss and a hybrid cross-modality feature decoder. The multi-stage cross-modality contrastive loss is utilized to enhance the discriminability of the paired features and separate the unpaired features. Furthermore, the hybrid cross-modality feature decoder conducts local and non-local cross-modality feature interaction by a local cross-modality fusion module and a non-local cross-modality fusion module, respectively. Experimental results show that our method achieved state-of-the-art results on two public medical image segmentation datasets.

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JBHI Journal 2025 Journal Article

MIT-SAM: Medical Image-Text SAM With Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation

Xichuan Zhou
Lingfeng Yan
Rui Ding
Chukwuemeka Clinton Atabansi
Jing Nie
Lihui Chen
Yujie Feng
Haijun Liu

In recent times, leveraging lesion text as supplementary data to enhance the performance of medical image segmentation models has garnered attention. Previous approaches only used attention mechanisms to integrate image and text features, while not effectively utilizing the highly condensed textual semantic information in improving the fused features, resulting in inaccurate lesion segmentation. This paper introduces a novel approach, the Medical Image-Text Segment Anything Model (MIT-SAM), for text-assisted medical image segmentation. Specifically, we introduce the SAM-enhanced image encoder and a Bert-based text encoder to extract heterogeneous features. To better leverage the highly condensed textual semantic information for heterogeneous feature fusion, such as crucial details like position and quantity, we propose the image-text interactive fusion (ITIF) block and self-supervised text reconstruction (SSTR) method. The ITIF block facilitates the mutual enhancement of homogeneous information among heterogeneous features and the SSTR method empowers the model to capture crucial details concerning lesion text, including location, quantity, and other key aspects. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on the QaTa-COV19 and MosMedData+ datasets.

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AAAI Conference 2021 Conference Paper

Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization

Xichuan Zhou
Kui Liu
Cong Shi
Haijun Liu
Ji Liu

Recent researches on information theory shed new light on the continuous attempts to open the black box of neural signal encoding. Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Bottleneck approach for quantizing and encoding neural network activations. Based on the rate-distortion theory, the Bitwise Bottleneck attempts to determine the most significant bits in activation representation by assigning and approximating the sparse coefficients associated with different bits. Given the constraint of a limited average code rate, the bottleneck minimizes the distortion for optimal activation quantization in a flexible layer-by-layer manner. Experiments over ImageNet and other datasets show that, by minimizing the quantization distortion of each layer, the neural network with bottlenecks achieves the state-of-the-art accuracy with low-precision activation. Meanwhile, by reducing the code rate, the proposed method can improve the memory and computational efficiency by over six times compared with the deep neural network with standard single-precision representation. The source code is available on GitHub: https: //github. com/CQUlearningsystemgroup/BitwiseBottleneck.

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