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学者姓名：林丽群
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Abstract ：
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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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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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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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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Natural video capturing suffers from visual blurriness due to high-motion of cameras or objects. Until now, the video blurriness removal task has been extensively explored for both human vision and machine processing. However, its computational cost is still a critical issue and has not yet been fully addressed. In this paper, we propose a novel Lightweight Video Deblurring (LightViD) method that achieves the top-tier performance with an extremely low parameter size. The proposed LightViD consists of a blur detector and a deblurring network. In particular, the blur detector effectively separate blurriness regions, thus avoid both unnecessary computation and over-enhancement on non-blurriness regions. The deblurring network is designed as a lightweight model. It employs a Spatial Feature Fusion Block (SFFB) to extract hierarchical spatial features, which are further fused by ConvLSTM for effective spatial-temporal feature representation. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our LightViD method, which achieves competitive performances on GoPro and DVD datasets, with reduced computational costs of 1.63M parameters and 96.8 GMACs. Trained model available: https://github.com/wgp/LightVid.
Keyword ：
blur detection blur detection Computational efficiency Computational efficiency Computational modeling Computational modeling Detectors Detectors Feature extraction Feature extraction Image restoration Image restoration Kernel Kernel spatial-temporal feature fusion spatial-temporal feature fusion Task analysis Task analysis Video deblurring Video deblurring
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| GB/T 7714 | Lin, Liqun , Wei, Guangpeng , Liu, Kanglin et al. LightViD: Efficient Video Deblurring With Spatial-Temporal Feature Fusion [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (8) : 7430-7439 . |
| MLA | Lin, Liqun et al. "LightViD: Efficient Video Deblurring With Spatial-Temporal Feature Fusion" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 8 (2024) : 7430-7439 . |
| APA | Lin, Liqun , Wei, Guangpeng , Liu, Kanglin , Feng, Wanjian , Zhao, Tiesong . LightViD: Efficient Video Deblurring With Spatial-Temporal Feature Fusion . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (8) , 7430-7439 . |
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Due to the variability of the network environment, video playback is prone to lag and bit rate fluctuations, which seriously affects the quality of end-user experience. In order to optimize network resource allocation and enhance user viewing experience, it is crucial to accurately evaluate video quality. Existing video quality evaluation methods mainly focus on the visual perception characteristics of short videos, with less consideration of the ability of human memory characteristics to store and express visual information, and the interaction between visual perception and memory characteristics. In contrast, when users watch long videos, video quality evaluation needs dynamic evaluation, which needs to consider both perceptual and memory elements. To better measure the quality evaluation of long videos, we introduce a deep network model to deeply explore the impact of video perception and memory characteristics on users' viewing experience, and proposes a dynamic quality evaluation model for long videos based on these two characteristics. Firstly, we design subjective experiments to investigate the influence of visual perceptual features and human memory features on user experience quality under different video playback modes, and constructs a video quality database with perception and memory (PAM-VQD) based on user perception and memory. Secondly, based on the PAM-VQD database, a deep learning methodology is utilized to extract deep perceptual features of videos, combined with visual attention mechanism, in order to accurately evaluate the impact of perception on user experience quality. Finally, the three features of perceptual quality score, playback status and self-lag interval output from the front-end network are fed into the long short-term memory network to establish the temporal dependency between visual perception and memory features. The experimental results show that the proposed quality assessment model can accurately predict the user experience quality under different video playback modes with good generalization performance. © 2024 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Long short-term memory Long short-term memory Memory architecture Memory architecture Resource allocation Resource allocation Video analysis Video analysis Video recording Video recording
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| GB/T 7714 | Lin, Li-Qun , Ji, Shu-Yi , He, Jia-Chen et al. Research of Video Dynamic Quality Evaluation Based on Human Perception and Memory [J]. | Acta Electronica Sinica , 2024 , 52 (11) : 3727-3740 . |
| MLA | Lin, Li-Qun et al. "Research of Video Dynamic Quality Evaluation Based on Human Perception and Memory" . | Acta Electronica Sinica 52 . 11 (2024) : 3727-3740 . |
| APA | Lin, Li-Qun , Ji, Shu-Yi , He, Jia-Chen , Zhao, Tie-Song , Chen, Wei-Ling , Guo, Chong-Ming . Research of Video Dynamic Quality Evaluation Based on Human Perception and Memory . | Acta Electronica Sinica , 2024 , 52 (11) , 3727-3740 . |
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Abstract ：
在"中国制造2025"的国家需求及福建省海西地方经济和产业升级需求的背景下,传统的信号与信息处理专业的培养方式对未来所需的人才品质存在不适应性.通过分析信号与信息处理专业教学体系现状,以福州大学为例,研究人工智能时代的信号专业教育教学改革机制,分别从学位点建设、课程建设、培养方案、培养目标、课程体系等方面探讨了教学改革机制,从而为高等院校培养信号与信息处理方向的综合型创新人才提供参考.
Keyword ：
5G 5G 人工智能 人工智能 信号与信息处理专业 信号与信息处理专业 教学改革 教学改革 课程思政 课程思政
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| GB/T 7714 | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 [J]. | 教育教学论坛 , 2024 , (40) : 42-46 . |
| MLA | 陈炜玲 et al. ""5G+人工智能"时代的教学新挑战" . | 教育教学论坛 40 (2024) : 42-46 . |
| APA | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 . | 教育教学论坛 , 2024 , (40) , 42-46 . |
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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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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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