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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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Low-light image enhancement (LLIE) is a challenging task, due to the multiple degradation problems involved, such as low brightness, color distortion, heavy noise, and detail degradation. Existing deep learning-based LLIE methods mainly use encoder-decoder networks or full-resolution networks, which excel at extracting context or detail information, respectively. Since detail and context information are both required for LLIE, existing methods cannot solve all the degradation problems. To solve the above problem, we propose an LLIE method based on collaboratively enhanced and integrated detail-context information (CoEIDC). Specifically, we propose a full-resolution network with two collaborative subnetworks, namely the detail extraction and enhancement subnetwork (DE2-Net) and context extraction and enhancement subnetwork (CE2-Net). CE2-Net extracts context information from the features of DE2-Net at different stages through large receptive field convolutions. Moreover, a collaborative attention module (CAM) and a detail-context integration module are proposed to enhance and integrate detail and context information. CAM is reused to enhance the detail features from multi-receptive fields and the context features from multiple stages. Extensive experimental results demonstrate that our method outperforms the state-of-the-art LLIE methods, and is applicable to other image enhancement tasks, such as underwater image enhancement.
Keyword ：
Collaborative enhancement and integration Collaborative enhancement and integration Color/brightness correction Color/brightness correction Detail reconstruction Detail reconstruction Low-light image enhancement Low-light image enhancement
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| GB/T 7714 | Niu, Yuzhen , Lin, Xiaofeng , Xu, Huangbiao et al. Collaboratively enhanced and integrated detail-context information for low enhancement [J]. | PATTERN RECOGNITION , 2025 , 162 . |
| MLA | Niu, Yuzhen et al. "Collaboratively enhanced and integrated detail-context information for low enhancement" . | PATTERN RECOGNITION 162 (2025) . |
| APA | Niu, Yuzhen , Lin, Xiaofeng , Xu, Huangbiao , Xu, Rui , Chen, Yuzhong . Collaboratively enhanced and integrated detail-context information for low enhancement . | PATTERN RECOGNITION , 2025 , 162 . |
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Image aesthetic assessment (IAA) has drawn wide attention in recent years. This task aims to predict the aesthetic quality of images by simulating human aesthetic perception mechanism, thereby assisting users in selecting images with higher aesthetic value. For IAA, the local information and various global semantic information contained in an image, such as composition, theme, and emotion, all play a crucial role. Existing CNN-based methods attempt to use multi-branch strategies to extract local and global semantic information related to IAA from images. However, these methods can only extract limited and specific global semantic information, and requiring additional labeled datasets. Furthermore, some cross-modal IAA methods have been proposed to use both images and user comments, but they often fail to fully explore the valuable information within each modality and the correlations between cross-modal features, affecting cross-modal IAA accuracy. Considering these limitations, in this paper, we propose a cross-modal IAA model that progressively fuses local and global image features. The model consists of a progressive local and global image feature fusion branch, a text feature enhancement branch, and a cross-modal feature fusion module. In the image branch, we introduce an inter-layer feature fusion module (IFFM) and adopt a progressive way to interact and fuse the extracted local and global features to obtain more comprehensive image features. In the text branch, we propose a text feature enhancement module (TFEM) to strengthen the extracted text features, so as to mine more effective textual information. Meanwhile, considering the intrinsic correlation between image and text features, we propose a cross-modal feature fusion module (CFFM) to integrate and fuse image features with text features for aesthetic assessment. Experimental results on the AVA (Aesthetic Visual Analysis) dataset validate the superiority of our method for IAA task.
Keyword ：
Cross-modality Cross-modality Feature fusion Feature fusion Image aesthetic assessment Image aesthetic assessment Local and global features Local and global features Textual information Textual information
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| GB/T 7714 | Niu, Yuzhen , Chen, Siling , Chen, Shanshan et al. Progressive fusion of local and global image features for cross-modal image aesthetic assessment [J]. | MULTIMEDIA SYSTEMS , 2025 , 31 (2) . |
| MLA | Niu, Yuzhen et al. "Progressive fusion of local and global image features for cross-modal image aesthetic assessment" . | MULTIMEDIA SYSTEMS 31 . 2 (2025) . |
| APA | Niu, Yuzhen , Chen, Siling , Chen, Shanshan , Li, Fusheng . Progressive fusion of local and global image features for cross-modal image aesthetic assessment . | MULTIMEDIA SYSTEMS , 2025 , 31 (2) . |
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Enhancing the quality of underwater images is crucial for advancements in the fields of underwater exploration and underwater rescue. Existing underwater image enhancement methods typically rely on paired underwater images and reference images for training. However, obtaining corresponding reference images for underwater images is challenging in practice. In contrast, acquiring high-quality unpaired underwater images or images captured on land are relatively more straightforward. Furthermore, existing techniques for underwater image enhancement often struggle to address a variety of distortion types simultaneously. To avoid the reliance on paired training data, reduce the difficulty of acquiring training data, and effectively handle diverse types of underwater image distortions, in this paper, we propose a novel unpaired underwater image enhancement method based on the frequency-decomposed generative adversarial network (FD-GAN). We design a dual-branch generator based on high and low frequencies to reconstruct high-quality underwater images. Specifically, feature-level wavelet transform is introduced to separate the features into low-frequency and high-frequency parts. Then the separated features are processed by a cycle-consistent generative adversarial network, so as to simultaneously enhance the color and luminance in the low-frequency component and details in the high-frequency part. More specific, the low-frequency branch employs an encoder-decoder structure with a low-frequency attention mechanism to enhance the color and brightness of the image. The high-frequency branch utilizes parallel high-frequency attention mechanisms to enhance various high-frequency components, thereby achieving the restoration of image details. Experimental results on multiple datasets show that the proposed method trained with unpaired high-quality underwater images or unpaired high-quality underwater images and on-land images, can effectively generate high-quality underwater enhanced images and the proposed method is superior to the state-of-the-art underwater image enhancement methods in terms of effectiveness and generalization. © 2025 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Color image processing Color image processing Image coding Image coding Image compression Image compression Image enhancement Image enhancement Photointerpretation Photointerpretation Underwater photography Underwater photography Wavelet decomposition Wavelet decomposition
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| GB/T 7714 | Niu, Yu-Zhen , Zhang, Ling-Xin , Lan, Jie et al. FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement [J]. | Acta Electronica Sinica , 2025 , 53 (2) : 527-544 . |
| MLA | Niu, Yu-Zhen et al. "FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement" . | Acta Electronica Sinica 53 . 2 (2025) : 527-544 . |
| APA | Niu, Yu-Zhen , Zhang, Ling-Xin , Lan, Jie , Xu, Rui , Ke, Xiao . FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement . | Acta Electronica Sinica , 2025 , 53 (2) , 527-544 . |
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Camouflaged object detection (COD) aims to resolve the tough issue of accurately segmenting objects hidden in the surroundings. However, the existing methods suffer from two major problems: the incomplete interior and the inaccurate boundary of the object. To address these difficulties, we propose a three-stage skeletonboundary-guided network (SBGNet) for the COD task. Specifically, we design a novel skeleton-boundary label to be complementary to the typical pixel-wise mask annotation, emphasizing the interior skeleton and the boundary of the camouflaged object. Furthermore, the proposed feature guidance module (FGM) leverages the skeleton-boundary feature to guide the model to focus on both the interior and the boundary of the camouflaged object. Besides, we design a bidirectional feature flow path with the information interaction module (IIM) to propagate and integrate the semantic and texture information. Finally, we propose the dual feature distillation module (DFDM) to progressively refine the segmentation results in a fine-grained manner. Comprehensive experiments demonstrate that our SBGNet outperforms 20 state-of-the-art methods on three benchmarks in both qualitative and quantitative comparisons. CCS Concepts: center dot Computing methodologies -> Scene understanding;
Keyword ：
Bidirectional feature flow path Bidirectional feature flow path Camouflaged object detection Camouflaged object detection Feature distillation Feature distillation Skeleton-boundary guidance Skeleton-boundary guidance
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| GB/T 7714 | Niu, Yuzhen , Xu, Yeyuan , Li, Yuezhou et al. Skeleton-Boundary-Guided Network for Camouflaged Object Detection [J]. | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (3) . |
| MLA | Niu, Yuzhen et al. "Skeleton-Boundary-Guided Network for Camouflaged Object Detection" . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 21 . 3 (2025) . |
| APA | Niu, Yuzhen , Xu, Yeyuan , Li, Yuezhou , Zhang, Jiabang , Chen., Yuzhong . Skeleton-Boundary-Guided Network for Camouflaged Object Detection . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (3) . |
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Capturing images at night are susceptible to inadequate illumination conditions and motion blurring. Given the typical coupling of these two forms of degradation, a pioneer work takes a compact approach of brightening followed by deblurring. However, this sequential approach may compromise informative features and elevate the likelihood of generating unintended artifacts. In this paper, we observe that the co-existing low light and blurs intuitively impair multiple perceptions, making it difficult to produce visually appealing results. To meet these challenges, we propose perceptual decoupling with heterogeneous auxiliary tasks (PDHAT) for joint low-light image enhancement and deblurring. Based on the crucial perceptual properties of the two degradations, we construct two individual auxiliary tasks: coarse preview prediction (CPP) and high-frequency reconstruction (HFR), so that the perception of color, brightness, edges, and details are decoupled into heterogeneous auxiliary tasks to obtain task-specific representations for parallel assisting the main task: joint low-light enhancement and deblurring (LLE-Deblur). Furthermore, we develop dedicated modules to build the network blocks in each branch based on the exclusive properties of each task. Comprehensive experiments are conducted on LOL-Blur and Real-LOL-Blur datasets, showing that our method outperforms existing methods on quantitative metrics and qualitative results.
Keyword ：
Brightness Brightness Degradation Degradation Feature extraction Feature extraction image deblurring image deblurring Image enhancement Image enhancement Image reconstruction Image reconstruction Image restoration Image restoration joint solution joint solution low-light image enhancement low-light image enhancement Multiple degradations Multiple degradations Task analysis Task analysis
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| GB/T 7714 | Li, Yuezhou , Xu, Rui , Niu, Yuzhen et al. Perceptual Decoupling With Heterogeneous Auxiliary Tasks for Joint Low-Light Image Enhancement and Deblurring [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 6663-6675 . |
| MLA | Li, Yuezhou et al. "Perceptual Decoupling With Heterogeneous Auxiliary Tasks for Joint Low-Light Image Enhancement and Deblurring" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 6663-6675 . |
| APA | Li, Yuezhou , Xu, Rui , Niu, Yuzhen , Guo, Wenzhong , Zhao, Tiesong . Perceptual Decoupling With Heterogeneous Auxiliary Tasks for Joint Low-Light Image Enhancement and Deblurring . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 6663-6675 . |
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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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Photos captured under low-light conditions suffer from multiple coupling problems, i.e., low brightness, color distortion, heavy noise, and detail degradation, making low-light image enhancement a challenging task. Existing deep learning-based low-light image enhancement methods typically focus on improving the illumination and color while neglecting the noise in the enhanced image. To solve the above problems, this paper proposes a low-light image enhancement method based on task decoupling. According to the different requirements for high-level and low-level features, the low-light image enhancement task is decoupled into two subtasks: illumination and color enhancement and detail reconstruction. Based on the task decoupling, we propose a two-branch low-light image enhancement network (TBLIEN). The illumination and color enhancement branch is built as a U-Net structure with global feature extraction, which exploits deep semantic information for illumination and color improvement. The detail reconstruction branch uses a fully convolutional network that preserves the original resolution while performing detail restoration and noise removal. In addition, for the detail reconstruction branch, we design a half-dual attention residual module. Our module enhances features through spatial and channel attention mechanisms while preserving their context, allowing precise detail reconstruction. Extensive experiments on real and synthetic datasets show that our model outperforms other state-of-the-art methods, and has better generalization capability. Our method is also applicable to other image enhancement tasks, i.e., underwater image enhancement. © 2024 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Color Color Deep learning Deep learning Image enhancement Image enhancement Semantics Semantics
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| GB/T 7714 | Niu, Yu-Zhen , Chen, Ming-Ming , Li, Yue-Zhou et al. Task Decoupling Guided Low-Light Image Enhancement [J]. | Acta Electronica Sinica , 2024 , 52 (1) : 34-45 . |
| MLA | Niu, Yu-Zhen et al. "Task Decoupling Guided Low-Light Image Enhancement" . | Acta Electronica Sinica 52 . 1 (2024) : 34-45 . |
| APA | Niu, Yu-Zhen , Chen, Ming-Ming , Li, Yue-Zhou , Zhao, Tie-Song . Task Decoupling Guided Low-Light Image Enhancement . | Acta Electronica Sinica , 2024 , 52 (1) , 34-45 . |
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低照度条件下拍摄的照片往往存在亮度低、颜色失真、噪声高、细节退化等多重耦合问题，因此低照度图像增强是一个具有挑战性的任务. 现有基于深度学习的低照度图像增强方法通常聚焦于对亮度和色彩的提升，导致增强图像中仍然存在噪声等缺陷. 针对上述问题，本文提出了一种基于任务解耦的低照度图像增强方法，根据低照度图像增强任务对高层和低层特征的不同需求，将该任务解耦为亮度与色彩增强和细节重构两组任务，进而构建双分支低照度图像增强网络模型（Two-Branch Low-light Image Enhancement Network，TBLIEN）. 其中，亮度与色彩增强分支采用带全局特征的U-Net结构，提取深层语义信息改善亮度与色彩；细节重构分支采用保持原始分辨率的全卷积网络实现细节复原和噪声去除. 此外，在细节重构分支中，本文提出一种半双重注意力残差模块，能在保留上下文特征的同时通过空间和通道注意力强化特征，从而实现更精细的细节重构. 在合成和真实数据集上的广泛实验表明，本文模型的性能超越了当前先进的低照度图像增强方法，并具有更好的泛化能力，且可适用于水下图像增强等其他图像增强任务.
Keyword ：
任务解耦 任务解耦 低照度图像增强 低照度图像增强 双分支网络模型 双分支网络模型 对比学习 对比学习 残差网络 残差网络
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| GB/T 7714 | 牛玉贞 , 陈铭铭 , 李悦洲 et al. 基于任务解耦的低照度图像增强方法 [J]. | 电子学报 , 2024 . |
| MLA | 牛玉贞 et al. "基于任务解耦的低照度图像增强方法" . | 电子学报 (2024) . |
| APA | 牛玉贞 , 陈铭铭 , 李悦洲 , 赵铁松 . 基于任务解耦的低照度图像增强方法 . | 电子学报 , 2024 . |
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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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