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学者姓名：牛玉贞
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Abstract ：
增强水下图像质量对水下作业领域的发展具有重要意义 . 现有的水下图像增强方法通常基于成对的水下图像和参考图像进行训练，然而实际获取与水下图像对应的参考图像比较困难，相比之下获得非成对高质量水下图像或者陆上图像较为容易. 此外，现有的水下图像增强方法很难同时针对各种失真类型进行图像增强. 为了避免对成对训练数据的依赖和进一步降低获得训练数据的难度，并应对多样的水下图像失真类型，本文提出了一种基于分频式生成对抗网络（Frequency-Decomposed Generative Adversarial Network，FD-GAN）的非成对水下图像增强方法，并在此基础上设计了高低频双分支生成器用于重建高质量水下增强图像. 具体来说，本文引入特征级别的小波变换将特征分为低频和高频部分，并基于循环一致性生成对抗网络对低频和高频部分区分处理. 其中，低频分支采用结合低频注意力机制的编码-解码器结构实现对图像颜色和亮度的增强，高频分支则采用并行的高频注意力机制对各高频分量进行增强，从而实现对图像细节的恢复. 在多个标准水下图像数据集上的实验结果表明，本文提出的方法在使用非成对的高质量水下图像和引入部分陆上图像的情况下，均能有效生成高质量的水下增强图像，且有效性和泛化性均优于当 前主流的水下图像增强方法.
Keyword ：
小波变换 小波变换 水下图像增强 水下图像增强 注意力机制 注意力机制 生成对抗网络 生成对抗网络 高低频双分支生成器 高低频双分支生成器
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| GB/T 7714 | 牛玉贞 , 张凌昕 , 兰杰 et al. 基于分频式生成对抗网络的非成对水下图像增强 [J]. | 电子学报 , 2025 . |
| MLA | 牛玉贞 et al. "基于分频式生成对抗网络的非成对水下图像增强" . | 电子学报 (2025) . |
| APA | 牛玉贞 , 张凌昕 , 兰杰 , 许瑞 , 柯逍 . 基于分频式生成对抗网络的非成对水下图像增强 . | 电子学报 , 2025 . |
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The problem of video demoiréing is a new challenge in video restoration. Unlike image demoiréing, which involves removing static and uniform patterns, video demoiréing requires tackling dynamic and varied moiré patterns while maintaining video details, colors, and temporal consistency. It is particularly challenging to model moiré patterns for videos with camera or object motions, where separating moiré from the original video content across frames is extremely difficult. Nonetheless, we observe that the spatial distribution of moiré patterns is often sparse on each frame, and their long-range temporal correlation is not significant. To fully leverage this phenomenon, a sparsity-constrained spatial self-attention scheme is proposed to concentrate on removing sparse moiré efficiently for each frame without being distracted by dynamic video content. The frame-wise spatial features are then correlated and aggregated via the local temporal cross-frame-attention module to produce temporal-consistent high-quality moiré-free videos. The above decoupled spatial and temporal transformers constitute the Spatio-Temporal Decomposition Network, dubbed STD-Net. For evaluation, we present a large-scale video demoiréing benchmark featuring various real-life scenes, camera motions, and object motions. We demonstrate that our proposed model can effectively and efficiently achieve superior performance on video demoiréing and single image demoiréing tasks. The proposed dataset is released at https://github.com/FZU-N/LVDM. © 1991-2012 IEEE.
Keyword ：
Cameras Cameras Image reconstruction Image reconstruction Job analysis Job analysis Large datasets Large datasets Restoration Restoration Video recording Video recording
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| GB/T 7714 | Niu, Yuzhen , Xu, Rui , Lin, Zhihua et al. STD-Net: Spatio-Temporal Decomposition Network for Video Demoiréing With Sparse Transformers [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (9) : 8562-8575 . |
| MLA | Niu, Yuzhen et al. "STD-Net: Spatio-Temporal Decomposition Network for Video Demoiréing With Sparse Transformers" . | IEEE Transactions on Circuits and Systems for Video Technology 34 . 9 (2024) : 8562-8575 . |
| APA | Niu, Yuzhen , Xu, Rui , Lin, Zhihua , Liu, Wenxi . STD-Net: Spatio-Temporal Decomposition Network for Video Demoiréing With Sparse Transformers . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (9) , 8562-8575 . |
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Low-light image enhancement is a challenging task due to the limited visibility in dark environments. While recent advances have shown progress in integrating CNNs and Transformers, the inadequate local-global perceptual interactions still impedes their application in complex degradation scenarios. To tackle this issue, we propose BiFormer, a lightweight framework that facilitates local-global collaborative perception via bilateral interaction. Specifically, our framework introduces a core CNN-Transformer collaborative perception block (CPB) that combines local-aware convolutional attention (LCA) and global-aware recursive transformer (GRT) to simultaneously preserve local details and ensure global consistency. To promote perceptual interaction, we adopt bilateral interaction strategy for both local and global perception, which involves local-to-global second-order interaction (SoI) in the dual-domain, as well as a mixed-channel fusion (MCF) module for global-to-local interaction. The MCF is also a highly efficient feature fusion module tailored for degraded features. Extensive experiments conducted on low-level and high-level tasks demonstrate that BiFormer achieves state-of-the-art performance. Furthermore, it exhibits a significant reduction in model parameters and computational cost compared to existing Transformer-based low-light image enhancement methods. IEEE
Keyword ：
bilateral interaction bilateral interaction Collaboration Collaboration Convolutional neural networks Convolutional neural networks hybrid CNN-Transformer hybrid CNN-Transformer Image enhancement Image enhancement Lighting Lighting Low-light image enhancement Low-light image enhancement mixed-channel fusion mixed-channel fusion Task analysis Task analysis Transformers Transformers Visualization Visualization
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| GB/T 7714 | Xu, R. , Li, Y. , Niu, Y. et al. Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement [J]. | IEEE Transactions on Multimedia , 2024 , 26 : 1-13 . |
| MLA | Xu, R. et al. "Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement" . | IEEE Transactions on Multimedia 26 (2024) : 1-13 . |
| APA | Xu, R. , Li, Y. , Niu, Y. , Xu, H. , Chen, Y. , Zhao, T. . Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement . | IEEE Transactions on Multimedia , 2024 , 26 , 1-13 . |
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Unmanned aerial vehicle (UAV)-based visual systems suffer from poor perception at nighttime. There are three challenges for enlightening nighttime vision for UAVs: First, the UAV nighttime images differ from underexposed images in the statistical characteristic, limiting the performance of general low-light image enhancement (LLIE) methods. Second, when enlightening nighttime images, the artifacts tend to be amplified, distracting the visual perception of UAVs. Third, due to the inherent scarcity of paired data in the real world, it is difficult for UAV nighttime vision to benefit from supervised learning. To meet these challenges, we propose a zero-referenced enlightening and restoration network (ZERNet) for improving the perception of UAV vision at nighttime. Specifically, by estimating the nighttime enlightening map (NE-map), a pixel-to-pixel transformation is then conducted to enlighten the dark pixels while suppressing overbright pixels. Furthermore, we propose the self-regularized restoration to preserve the semantic contents and restrict the artifacts in the final result. Finally, our method is derived from zero-referenced learning, which is free from paired training data. Comprehensive experiments show that the proposed ZERNet effectively improves the nighttime visual perception of UAVs on quantitative metrics, qualitative comparisons, and application-based analysis.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Image coding Image coding Image enlightening Image enlightening image restoration image restoration Image restoration Image restoration nighttime image nighttime image Semantics Semantics Training Training Training data Training data unmanned aerial vehicle (UAV) vision unmanned aerial vehicle (UAV) vision Visual perception Visual perception zero-referenced learning zero-referenced learning
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| GB/T 7714 | Li, Yuezhou , Niu, Yuzhen , Xu, Rui et al. Zero-Referenced Enlightening and Restoration for UAV Nighttime Vision [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
| MLA | Li, Yuezhou et al. "Zero-Referenced Enlightening and Restoration for UAV Nighttime Vision" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21 (2024) . |
| APA | Li, Yuezhou , Niu, Yuzhen , Xu, Rui , He, Yuqi . Zero-Referenced Enlightening and Restoration for UAV Nighttime Vision . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
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与从现实场景中拍摄的自然图像不同，屏幕内容图像是一种合成图像，通常由计算机生成的文本、图形和动画等各种多媒体形式组合而成. 现有评估方法通常未能充分考虑图像边缘结构信息和全局上下文信息对屏幕内容图像质量感知的影响. 为解决上述问题，本文提出一种基于边缘辅助和多尺度Transformer的无参考屏幕内容图像质量评估模型. 首先，使用高斯拉普拉斯算子构造由失真屏幕内容图像高频信息组成的边缘结构图，然后通过卷积神经网络对输入的失真屏幕内容图像和相应的边缘结构图进行多尺度的特征提取与融合，以图像的边缘结构信息为模型训练提供额外的信息增益. 此外，本文进一步构建了基于Transformer的多尺度特征编码模块，从而在CNN获得的局部特征基础上更好地建模不同尺度图像和边缘特征的全局上下文信息. 实验结果表明，本文提出的方法在指标上优于其他现有的无参考和全参考屏幕内容图像质量评估方法，能够取得更高的主客观视觉感知一致性.
Keyword ：
Transformer Transformer 卷积神经网络 卷积神经网络 多尺度特征 多尺度特征 无参考屏幕内容图像质量评估 无参考屏幕内容图像质量评估 高斯拉普拉斯算子 高斯拉普拉斯算子
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| GB/T 7714 | 陈羽中 , 陈友昆 , 林闽沪 et al. 基于边缘辅助和多尺度Transformer的无参考屏幕内容图像质量评估 [J]. | 电子学报 , 2024 . |
| MLA | 陈羽中 et al. "基于边缘辅助和多尺度Transformer的无参考屏幕内容图像质量评估" . | 电子学报 (2024) . |
| APA | 陈羽中 , 陈友昆 , 林闽沪 , 牛玉贞 . 基于边缘辅助和多尺度Transformer的无参考屏幕内容图像质量评估 . | 电子学报 , 2024 . |
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Low-light image enhancement is a challenging task due to the limited visibility in dark environments. While recent advances have shown progress in integrating CNNs and Transformers, the inadequate local-global perceptual interactions still impedes their application in complex degradation scenarios. To tackle this issue, we propose BiFormer, a lightweight framework that facilitates local-global collaborative perception via bilateral interaction. Specifically, our framework introduces a core CNN-Transformer collaborative perception block (CPB) that combines local-aware convolutional attention (LCA) and global-aware recursive Transformer (GRT) to simultaneously preserve local details and ensure global consistency. To promote perceptual interaction, we adopt bilateral interaction strategy for both local and global perception, which involves local-to-global second-order interaction (SoI) in the dual-domain, as well as a mixed-channel fusion (MCF) module for global-to-local interaction. The MCF is also a highly efficient feature fusion module tailored for degraded features. Extensive experiments conducted on low-level and high-level tasks demonstrate that BiFormer achieves state-of-the-art performance. Furthermore, it exhibits a significant reduction in model parameters and computational cost compared to existing Transformer-based low-light image enhancement methods.
Keyword ：
bilateral interaction bilateral interaction hybrid CNN- Transformer hybrid CNN- Transformer Low-light image enhancement Low-light image enhancement mixed-channel fusion mixed-channel fusion
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| GB/T 7714 | Xu, Rui , Li, Yuezhou , Niu, Yuzhen et al. Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 10792-10804 . |
| MLA | Xu, Rui et al. "Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 10792-10804 . |
| APA | Xu, Rui , Li, Yuezhou , Niu, Yuzhen , Xu, Huangbiao , Chen, Yuzhong , Zhao, Tiesong . Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 10792-10804 . |
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Defocus deblurring using dual-pixel sensors has gathered significant attention in recent years. However, current methodologies have not adequately addressed the challenge of defocus disparity between dual views, resulting in suboptimal performance in recovering details from severely defocused pixels. To counteract this limitation, we introduce in this paper a parallax-aware dual-view feature enhancement and adaptive detail compensation network (PA-Net), specifically tailored for dual-pixel defocus deblurring task. Our proposed PA- Net leverages an encoder-decoder architecture augmented with skip connections, designed to initially extract distinct features from the left and right views. A pivotal aspect of our model lies at the network's bottleneck, where we introduce a parallax-aware dual-view feature enhancement based on Transformer blocks, which aims to align and enhance extracted dual-pixel features, aggregating them into a unified feature. Furthermore, taking into account the disparity and the rich details embedded in encoder features, we design an adaptive detail compensation module to adaptively incorporate dual-view encoder features into image reconstruction, aiding in restoring image details. Experimental results demonstrate that our proposed PA-Net exhibits superior performance and visual effects on the real-world dataset.
Keyword ：
Defocus deblurring Defocus deblurring Defocus disparity Defocus disparity Detail restoration Detail restoration Dual-pixel Dual-pixel Image restoration Image restoration
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| GB/T 7714 | Niu, Yuzhen , He, Yuqi , Xu, Rui et al. Parallax-aware dual-view feature enhancement and adaptive detail compensation for dual-pixel defocus deblurring [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 139 . |
| MLA | Niu, Yuzhen et al. "Parallax-aware dual-view feature enhancement and adaptive detail compensation for dual-pixel defocus deblurring" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 139 (2024) . |
| APA | Niu, Yuzhen , He, Yuqi , Xu, Rui , Li, Yuezhou , Chen, Yuzhong . Parallax-aware dual-view feature enhancement and adaptive detail compensation for dual-pixel defocus deblurring . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 139 . |
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Pedestrian Attribute Recognition (PAR) involves identifying the attributes of individuals in person images. Existing PAR methods typically rely on CNNs as the backbone network to extract pedestrian features. However, CNNs process only one adjacent region at a time, leading to the loss of long-range inter-relations between different attribute-specific regions. To address this limitation, we leverage the Vision Transformer (ViT) instead of CNNs as the backbone for PAR, aiming to model long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose two novel components: the Selective Feature Activation Method (SFAM) and the Orthogonal Feature Activation Loss. SFAM smartly suppresses the more informative attribute-specific features, compelling the PAR model to capture discriminative features from regions that are easily overlooked. The proposed loss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the complementarity of features in space. We conduct experiments on several benchmark PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrating the effectiveness of our method. Specifically, our method outperforms existing state-of-the-art approaches by GRL, IAA-Caps, ALM, and SSC in terms of mA on the four datasets, respectively. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Chemical activation Chemical activation
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| GB/T 7714 | Wu, Junyi , Huang, Yan , Gao, Min et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition [C] . 2024 : 6039-6047 . |
| MLA | Wu, Junyi et al. "Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition" . (2024) : 6039-6047 . |
| APA | Wu, Junyi , Huang, Yan , Gao, Min , Niu, Yuzhen , Yang, Mingjing , Gao, Zhipeng et al. Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition . (2024) : 6039-6047 . |
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伪装目标检测旨在检测隐藏在复杂环境中的高度隐蔽物体，在医学、农业等多个领域有重要应用价值.现有方法结合边界先验过分强调边界区域，对伪装目标内部信息的表征不足，导致模型对伪装目标的内部区域检测不准确.同时，已有方法缺乏对伪装目标前景特征的有效挖掘，使背景区域被误检为伪装目标.为解决上述问题，本文提出一种基于边界特征融合和前景引导的伪装目标检测方法，该方法由特征提取、边界特征融合、主干特征增强和预测等若干个阶段构成.在边界特征融合阶段，首先，通过边界特征提取模块获得边界特征并预测边界掩码；然后，边界特征融合模块将边界特征和边界掩码与最低层次的主干特征有效融合；同时，加强伪装目标边界位置及内部区域特征.此外，设计前景引导模块，利用预测的伪装目标掩码增强主干特征，即将前一层特征预测的伪装目标掩码作为当前层特征的前景注意力，并对特征执行空间交互，提升网络对空间关系的识别能力，使网络关注精细而完整的伪装目标区域.本文在4个广泛使用的基准数据集上的实验结果表明，提出的方法优于对比的19个主流方法，对伪装目标检测任务具有更强鲁棒性和泛化能力.
Keyword ：
伪装目标检测 伪装目标检测 前景引导 前景引导 空间交互 空间交互 边界先验 边界先验 边界掩码 边界掩码 边界特征 边界特征
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| GB/T 7714 | 刘文犀 , 张家榜 , 李悦洲 et al. 基于边界特征融合和前景引导的伪装目标检测 [J]. | 电子学报 , 2024 , 52 (07) : 2279-2290 . |
| MLA | 刘文犀 et al. "基于边界特征融合和前景引导的伪装目标检测" . | 电子学报 52 . 07 (2024) : 2279-2290 . |
| APA | 刘文犀 , 张家榜 , 李悦洲 , 赖宇 , 牛玉贞 . 基于边界特征融合和前景引导的伪装目标检测 . | 电子学报 , 2024 , 52 (07) , 2279-2290 . |
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Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group.
Keyword ：
co-localization co-localization co-segmentation co-segmentation Feature extraction Feature extraction Fuses Fuses graph embedding graph embedding Image segmentation Image segmentation Location awareness Location awareness Multi-view learning Multi-view learning Semantics Semantics sparse constraint sparse constraint Task analysis Task analysis Visualization Visualization
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| GB/T 7714 | Huang, Aiping , Li, Lijian , Zhang, Le et al. Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (6) : 4942-4956 . |
| MLA | Huang, Aiping et al. "Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 6 (2024) : 4942-4956 . |
| APA | Huang, Aiping , Li, Lijian , Zhang, Le , Niu, Yuzhen , Zhao, Tiesong , Lin, Chia-Wen . Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (6) , 4942-4956 . |
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