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学者姓名：林丽群
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Abstract ：
Aerial imaging aims to produce well-exposed images with rich details. However, aerial photography may encounter low-light conditions during dusk or dawn, as well as on cloudy or foggy days. In such low-light scenarios, aerial images often suffer from issues such as underexposure, noise, and color distortion. Most existing low-light imaging methods struggle with achieving realistic exposure and retaining rich details. To address these issues, we propose an Aerial Low-light Imaging with Color-monochrome Engagement (ALICE), which employs a coarse-to-fine strategy to correct low-light aerial degradation. First, we introduce wavelet transform to design a perturbation corrector for coarse exposure recovery while preserving details. Second, inspired by the binocular low-light imaging mechanism of the Human Visual System (HVS), we introduce uniformly well-exposed monochrome images to guide a refinement restorer, processing luminance and chrominance branches separately for further improved reconstruction. Within this framework, we design a Reference-based Illumination Fusion Module (RIFM) and an Illumination Detail Transformation Module (IDTM) for targeted exposure and detail restoration. Third, we develop a Dual-camera Low-light Aerial Imaging (DuLAI) dataset to evaluate our proposed ALICE. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our ALICE, achieving a PSNR improvement of at least 19.52% over 12 state-of-the-art methods on the DuLAI Syn-R1440 dataset, while providing more balanced exposure and richer details. Our codes and datasets are available at https://github.com/yuanpengwu1/ALICE.
Keyword ：
Cameras Cameras Colored noise Colored noise Color-monochrome cameras Color-monochrome cameras Degradation Degradation Frequency modulation Frequency modulation Image color analysis Image color analysis Image restoration Image restoration Lighting Lighting low-light aerial imaging low-light aerial imaging Perturbation methods Perturbation methods Superresolution Superresolution Wavelet transforms Wavelet transforms
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| GB/T 7714 | Yuan, Pengwu , Lin, Liqun , Lin, Junhong et al. Low-Light Aerial Imaging With Color and Monochrome Cameras [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Yuan, Pengwu et al. "Low-Light Aerial Imaging With Color and Monochrome Cameras" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Yuan, Pengwu , Lin, Liqun , Lin, Junhong , Liao, Yipeng , Zhao, Tiesong . Low-Light Aerial Imaging With Color and Monochrome Cameras . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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With the advancement of digital photography, high-definition video has become crucial in daily life, professional production, and security monitoring. However, low light or small sensors often lead to noise in captured videos, impacting visual quality and subsequent processing. To address these issues, we propose a lightweight video denoising algorithm (LVDA). First, our method introduces SimpleGate, a variant of the gated linear cell that incorporates nonlinearity independently of sigma, enabling direct division and multiplication of feature maps in the channel dimension to reduce computational load while maintaining performance. Second, we present a simplified channel attention mechanism as an alternative to traditional complex channel attention, further enhancing network efficiency. Based on the SimpleGate, we propose a channel gating block to replace the residual dense block. Third, we adopt depthwise overparameterized convolution to replace traditional convolution, reducing computation and model parameters while maintaining network structure and performance. Comprehensive quantitative and qualitative experiments demonstrate the effectiveness of our LVDA. (c) 2025 SPIE and IS&T
Keyword ：
deep learning deep learning lightweighting lightweighting time complexity time complexity video denoising video denoising
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| GB/T 7714 | Zhuang, Zichen , Xia, Yiming , Zhang, Yixuan et al. LVDA: efficient video denoising algorithms with lightweight network [J]. | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (3) . |
| MLA | Zhuang, Zichen et al. "LVDA: efficient video denoising algorithms with lightweight network" . | JOURNAL OF ELECTRONIC IMAGING 34 . 3 (2025) . |
| APA | Zhuang, Zichen , Xia, Yiming , Zhang, Yixuan , Lin, Liqun . LVDA: efficient video denoising algorithms with lightweight network . | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (3) . |
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Video compression artifact removal focuses on enhancing the visual quality of compressed videos by mitigating visual distortions. However, existing methods often struggle to effectively capture spatio-temporal features and recover high-frequency details, due to their suboptimal adaptation to the characteristics of compression artifacts. To overcome these limitations, we propose a novel Spatio-Temporal and Frequency Fusion (STFF) framework. STFF incorporates three key components: Feature Extraction and Alignment (FEA), which employs SRU for effective spatiotemporal feature extraction; Bidirectional High-Frequency Enhanced Propagation (BHFEP), which integrates HCAB to restore high-frequency details through bidirectional propagation; and Residual High-Frequency Refinement (RHFR), which further enhances high-frequency information. Extensive experiments demonstrate that STFF achieves superior performance compared to state-of-the-art methods in both objective metrics and subjective visual quality, effectively addressing the challenges posed by video compression artifacts. Trained model available: https://github.com/Stars-WMX/STFF.
Keyword ：
Degradation Degradation Feature extraction Feature extraction Image coding Image coding Image restoration Image restoration Motion compensation Motion compensation Optical flow Optical flow Quality assessment Quality assessment Spatiotemporal phenomena Spatiotemporal phenomena Transformers Transformers video coding video coding Video compression Video compression Video compression artifact removal Video compression artifact removal video enhancement video enhancement video quality video quality
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| GB/T 7714 | Wang, Mingxing , Liao, Yipeng , Chen, Weiling et al. STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal [J]. | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) : 542-554 . |
| MLA | Wang, Mingxing et al. "STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal" . | IEEE TRANSACTIONS ON BROADCASTING 71 . 2 (2025) : 542-554 . |
| APA | Wang, Mingxing , Liao, Yipeng , Chen, Weiling , Lin, Liqun , Zhao, Tiesong . STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal . | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) , 542-554 . |
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A fast deblurring network, based on a high-performance convolutional network and pixel volume, is proposed to address the limitations of existing video deblurring algorithms, which often overly emphasize inter-frame information, leading to high algorithmic complexity. First, high-performance convolutional networks are utilized to prune the deblurring network, thereby reducing both the number of model parameters and computational complexity. To address the increased network computational complexity resulting from the extensive use of traditional two-dimensional convolutional layers, depthwise over-parameterized convolutions are employed to replace traditional convolutions. This substitution significantly reduces computational complexity without compromising the network's structure and performance. In addition, the Charbonnier loss function is used to approximate the mean absolute error (MAE) loss function to alleviate the over-smoothing problem. At the same time, the problem of non-differentiability of the MAE loss function at zero is solved by adding a constant, to enhance the visual quality of video images. Experimental results demonstrate that the proposed method delivers superior deblurring performance. Compared with the baseline pixel volume deblurring network framework, our method achieves a significant reduction in model complexity, demonstrating 28.73% fewer parameters and 59.96% lower floating-point operations, underscoring its theoretical significance. (c) 2025 SPIE and IS&T
Keyword ：
algorithmic complexity algorithmic complexity depthwise over-parameterized convolutions depthwise over-parameterized convolutions loss function loss function video deblurring video deblurring
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| GB/T 7714 | Xie, Shangxi , Xia, Yiming , Zhong, Wenqi et al. Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization [J]. | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
| MLA | Xie, Shangxi et al. "Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization" . | JOURNAL OF ELECTRONIC IMAGING 34 . 2 (2025) . |
| APA | Xie, Shangxi , Xia, Yiming , Zhong, Wenqi , Lin, Liqun , Fu, Mingjian . Lightweight pixel volume deblurring network: enhanced video deblurring via efficient architecture optimization . | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
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Video distortion seriously affects user experience and downstream tasks. Existing video restoration methods still suffer from high-frequency detail loss, limited spatio-temporal dependency modeling, and high computational complexity. In this letter, we propose a novel video restoration method based on full-frequency spatio-temporal information enhancement (FFSTIE). The proposed FFSTIE includes an implicit alignment module for accurate recovery of high-frequency details and a full-frequency feature reconstruction module for adaptive enhancement of frequency components. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our FFSTIE method. On the video deblurring dataset DVD, FFSTIE achieves 0.75% improvement in PSNR and 1.08% improvement in SSIM with 35% fewer parameters and 59% lower GMAC compared to VDTR (TCSVT'2023), achieving a balance between performance and efficiency. On the video denoising dataset DAVIS, FFSTIE achieves the best performance with an average of 35.36 PSNR and 0.9347 SSIM, surpassing existing unsupervised methods.
Keyword ：
Computational complexity Computational complexity Convolution Convolution Distortion Distortion Encoding Encoding Feature extraction Feature extraction Frequency-domain analysis Frequency-domain analysis Implicit alignment Implicit alignment Interpolation Interpolation Noise reduction Noise reduction Runtime Runtime spatio-temporal information enhancement spatio-temporal information enhancement Transformers Transformers video restoration video restoration
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| GB/T 7714 | Lin, Liqun , Wang, Jianhui , Wei, Guangpeng et al. FFSTIE: Video Restoration With Full-Frequency Spatio-Temporal Information Enhancement [J]. | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 : 571-575 . |
| MLA | Lin, Liqun et al. "FFSTIE: Video Restoration With Full-Frequency Spatio-Temporal Information Enhancement" . | IEEE SIGNAL PROCESSING LETTERS 32 (2025) : 571-575 . |
| APA | Lin, Liqun , Wang, Jianhui , Wei, Guangpeng , Wang, Mingxing , Zhang, Ang . FFSTIE: Video Restoration With Full-Frequency Spatio-Temporal Information Enhancement . | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 , 571-575 . |
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With the widespread use of E-paper technology, numerous documents are being digitized and displayed on E-paper screens. However, the display quality of degraded document images on E-paper often suffers from a lack of detail. To address this challenge, we introduce a mapping model that converts color images into E-paper display images. This model leverages U-Net++ as its backbone, integrating residual connectivity and dual attention modules. Given the presence of varying stroke thicknesses in document images, a fixed-size convolutional kernel is insufficient. Therefore, we propose multi-branch channels and spatial attention modules (MCSAM), which combines the selective kernel network (SKNet) with a spatial attention mechanism to adaptively select the appropriate convolutional kernel size based on font size. To demonstrate its effectiveness, we tested the mapped images on a custom E-paper display platform. Experimental results highlight the superior performance of our proposed method.
Keyword ：
attention attention binarization binarization E-paper display E-paper display image mapping image mapping U-Net plus plus U-Net plus plus
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| GB/T 7714 | Zhang, Xianbin , Pei, Shufan , Lin, Liqun et al. Learning-based image mapping for degraded documents on E-paper display [J]. | JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY , 2025 , 33 (6) : 801-808 . |
| MLA | Zhang, Xianbin et al. "Learning-based image mapping for degraded documents on E-paper display" . | JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY 33 . 6 (2025) : 801-808 . |
| APA | Zhang, Xianbin , Pei, Shufan , Lin, Liqun , Zhao, Xiaoyan , Xu, Jiawei , Zhao, Tiesong . Learning-based image mapping for degraded documents on E-paper display . | JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY , 2025 , 33 (6) , 801-808 . |
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The popularity of 360 degrees video is due to its realistic and immersive experience, but the higher resolution poses challenges for data transmission and storage. Existing compression schemes for 360 degrees videos mainly focus on spatial and temporal redundancy elimination, neglecting the removal of visual perception redundancy. To address this issue, we exploit the visual characteristics of 360 degrees equirectangular projection to extend the popular Just Noticeable Difference model to Spherical Just Noticeable Difference. Our modeling takes advantage of the following factors: regional masking factor, which employs an entropy-based region classification and separately characterizes contrast masking effects on different regions; latitude projection characteristics, which model the impact of pixel-level warping during equirectangular projection mapping; field of view attention factor, which reflects the attention variation of the human visual system on 360 degrees display. Subjective tests show that our Spherical Just Noticeable Difference model is consistent with user perceptions and also has a higher tolerance of distortions with reduced bit rates of 360 degrees pictures. Further experiments on Versatile Video Coding also demonstrate that the introduction of the proposed model significantly reduces bit rates with negligible loss in perceived visual quality.
Keyword ：
Just Noticeable Difference(JND) Just Noticeable Difference(JND) Video coding Video coding Video quality assessment Video quality assessment Visual attention Visual attention
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| GB/T 7714 | Lin, Liqun , Wang, Yanting , Liu, Jiaqi et al. SJND: A Spherical Just Noticeable Difference Modelling for 360° video coding [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2025 , 138 . |
| MLA | Lin, Liqun et al. "SJND: A Spherical Just Noticeable Difference Modelling for 360° video coding" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 138 (2025) . |
| APA | Lin, Liqun , Wang, Yanting , Liu, Jiaqi , Wei, Hongan , Chen, Bo , Chen, Weiling et al. SJND: A Spherical Just Noticeable Difference Modelling for 360° video coding . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2025 , 138 . |
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由于网络环境的多变性，视频播放过程中容易出现卡顿、比特率波动等情况，严重影响了终端用户的体验质量. 为优化网络资源分配并提升用户观看体验，准确评估视频质量至关重要. 现有的视频质量评价方法主要针对短视频，普遍关注人眼视觉感知特性，较少考虑人类记忆特性对视觉信息的存储和表达能力，以及视觉感知和记忆特性之间的相互作用. 而用户观看长视频的时候，其质量评价需要动态评价，除了考虑感知要素外，还要引入记忆要素.为了更好地衡量长视频的质量评价，本文引入深度网络模型，深入探讨了视频感知和记忆特性对用户观看体验的影响，并基于两者特性提出长视频的动态质量评价模型. 首先，本文设计主观实验，探究在不同视频播放模式下，视觉感知特性和人类记忆特性对用户体验质量的影响，构建了基于用户感知和记忆的视频质量数据库（Video Quality Database with Perception and Memory，PAM-VQD）；其次，基于 PAM-VQD 数据库，采用深度学习的方法，结合视觉注意力机制，提取视频的深层感知特征，以精准评估感知对用户体验质量的影响；最后，将前端网络输出的感知质量分数、播放状态以及自卡顿间隔作为三个特征输入长短期记忆网络，以建立视觉感知和记忆特性之间的时间依赖关系. 实验结果表明，所提出的质量评估模型在不同视频播放模式下均能准确预测用户体验质量，且泛化性能良好.
Keyword ：
体验质量 体验质量 注意力机制 注意力机制 深度学习 深度学习 视觉感知特性 视觉感知特性 记忆效应 记忆效应
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| GB/T 7714 | 林丽群 , 暨书逸 , 何嘉晨 et al. 基于感知和记忆的视频动态质量评价 [J]. | 电子学报 , 2024 . |
| MLA | 林丽群 et al. "基于感知和记忆的视频动态质量评价" . | 电子学报 (2024) . |
| APA | 林丽群 , 暨书逸 , 何嘉晨 , 赵铁松 , 陈炜玲 , 郭宗明 . 基于感知和记忆的视频动态质量评价 . | 电子学报 , 2024 . |
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Underwater images often suffer from color distortion, blurred details, and low contrast. Therefore, more researchers are exploring underwater image enhancement (UIE) methods. However, UIE models based on deep learning suffer from high computational complexity, thus limiting their integration into underwater devices. In this work, we propose a lightweight UIE network based on knowledge distillation (UKD-Net), which includes a teacher network (T-Net) and a student network (S-Net). T-Net uses our designed multi-scale fusion block and parallel attention block to achieve excellent performance. We utilize knowledge distillation technology to transfer the rich knowledge of the T-Net onto a deployable S-Net. Additionally, S-Net employs blueprint separable convolutions and multistage distillation block to reduce parameter count and computational complexity. Results demonstrate that our UKD-Net successfully achieves a lightweight model design while maintaining superior enhanced performance.
Keyword ：
knowledge distillation knowledge distillation lightweight lightweight underwater image enhancement underwater image enhancement
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| GB/T 7714 | Zhao, Xiaoyan , Cai, Xiaowen , Xue, Ying et al. UKD-Net: efficient image enhancement with knowledge distillation [J]. | JOURNAL OF ELECTRONIC IMAGING , 2024 , 33 (2) . |
| MLA | Zhao, Xiaoyan et al. "UKD-Net: efficient image enhancement with knowledge distillation" . | JOURNAL OF ELECTRONIC IMAGING 33 . 2 (2024) . |
| APA | Zhao, Xiaoyan , Cai, Xiaowen , Xue, Ying , Liao, Yipeng , Lin, Liqun , Zhao, Tiesong . UKD-Net: efficient image enhancement with knowledge distillation . | JOURNAL OF ELECTRONIC IMAGING , 2024 , 33 (2) . |
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Versatile Video Coding (VVC) introduces various advanced coding techniques and tools, such as QuadTree with nested Multi-type Tree (QTMT) partition structure, and outperforms High Efficiency Video Coding (HEVC) in terms of coding performance. However, the improvement of coding performance leads to an increase in coding complexity. In this paper, we propose a multi-feature fusion framework that integrates the rate-distortion-complexity optimization theory with deep learning techniques to reduce the complexity of QTMT partition for VVC inter-prediction. Firstly, the proposed framework extracts features of luminance, motion, residuals, and quantization information from video frames and then performs feature fusion through a convolutional neural network to predict the minimum partition size of Coding Units (CUs). Next, a novel rate-distortion-complexity loss function is designed to balance computational complexity and compression performance. Then, through this loss function, we can adjust various distributions of rate-distortion-complexity costs. This adjustment impacts the prediction bias of the network and sets constraints on different block partition sizes to facilitate complexity adjustment. Compared to anchor VTM-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}13.0, the proposed method saves the encoding time by 10.14% to 56.62%, with BDBR increase confined to a range of 0.31% to 6.70%. The proposed method achieves a broader range of complexity adjustments while ensuring coding performance, surpassing both traditional methods and deep learning-based methods.
Keyword ：
Block partition Block partition CNN CNN Complexity optimization Complexity optimization Multi-feature fusion Multi-feature fusion Versatile video coding Versatile video coding
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| GB/T 7714 | Wei, Xiaojie , Zeng, Hongji , Fang, Ying et al. Multi-feature fusion for efficient inter prediction in versatile video coding [J]. | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2024 , 21 (6) . |
| MLA | Wei, Xiaojie et al. "Multi-feature fusion for efficient inter prediction in versatile video coding" . | JOURNAL OF REAL-TIME IMAGE PROCESSING 21 . 6 (2024) . |
| APA | Wei, Xiaojie , Zeng, Hongji , Fang, Ying , Lin, Liqun , Chen, Weiling , Xu, Yiwen . Multi-feature fusion for efficient inter prediction in versatile video coding . | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2024 , 21 (6) . |
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