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学者姓名：赵铁松
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Sonar images are vital in ocean explorations but face transmission challenges due to limited bandwidth and unstable channels. The Just Noticeable Difference (JND) represents the minimum distortion detectable by human observers. By eliminating perceptual redundancy, JND offers a solution for efficient compression and accurate Image Quality Assessment (IQA) to enable reliable transmission. However, existing JND models prove inadequate for sonar images due to their unique redundancy distributions and the absence of pixel-level annotated data. To bridge these gaps, we propose the first sonar-specific, picture-level JND dataset and a weakly supervised JND model that infers pixel-level JND from picture-level annotations. Our approach starts with pretraining a perceptually lossy/lossless predictor, which collaborates with sonar image properties to drive an unsupervised generator producing Critically Distorted Images (CDIs). These CDIs maximize pixel differences while preserving perceptual fidelity, enabling precise JND map derivation. Furthermore, we systematically investigate JND-guided optimization for sonar image compression and IQA algorithms, demonstrating favorable performance enhancements. © 1991-2012 IEEE.
Keyword ：
Just Noticeable Difference (JND) Just Noticeable Difference (JND) Sonar Image Sonar Image Underwater Acoustic Transmission Underwater Acoustic Transmission Weakly Supervision Weakly Supervision
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| GB/T 7714 | Chen, W. , Lin, W. , Feng, Q. et al. Pixel-Level Just Noticeable Difference in Sonar Images: Modeling and Applications [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2025 . |
| MLA | Chen, W. et al. "Pixel-Level Just Noticeable Difference in Sonar Images: Modeling and Applications" . | IEEE Transactions on Circuits and Systems for Video Technology (2025) . |
| APA | Chen, W. , Lin, W. , Feng, Q. , Zhang, R. , Zhao, T. . Pixel-Level Just Noticeable Difference in Sonar Images: Modeling and Applications . | IEEE Transactions on Circuits and Systems for Video Technology , 2025 . |
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The key challenge of cross-modal salient object detection lies in the representational discrepancy between different modal inputs. Existing methods typically employ only one encoding mode, either constrained encoding to extract modality-shared characteristics, or unconstrained encoding to capture modality-specific traits. However, the use of a single paradigm limits the capability of capturing salient cues, thus leading to poor generalization of existing methods. We propose a novel learning paradigm named "Collaborating Constrained and Unconstrained Encodings" (CCUE) that integrates constrained and unconstrained feature extraction to discover richer salient cues. Accordingly, we establish a CCUE network (CCUENet) consisting of a constrained branch and an unconstrained branch. The representations at each level from these two branches are integrated in an Information Selection and Fusion (ISF) module. The novelty of this module lies in its selective fusion of the important information from each feature primarily based on the response degree, which enables the network to aggregate effective cues for saliency detection. In the network training stage, we propose a Multi-scale Boundary Information (MBI) loss, which can constrain the detection results to retain clear region boundaries and boost the model's robustness to variations in object scale. Under the supervision of MBI loss, CCUENet is able to output high-quality saliency maps. The experimental results show that CCUENet exhibits superior performance on RGB-T and RGB-D datasets.
Keyword ：
Adaptation models Adaptation models constrained features constrained features Encoding Encoding Feature extraction Feature extraction Fuses Fuses Imaging Imaging information selection and fusion information selection and fusion Lighting Lighting multi-scale boundary information loss multi-scale boundary information loss Object detection Object detection Object recognition Object recognition Saliency detection Saliency detection Salient object detection Salient object detection Training Training unconstrained features unconstrained features
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| GB/T 7714 | Yao, Zhaojian , Gao, Wei , Li, Ge et al. Collaborating Constrained and Unconstrained Encodings for Cross-Modal Salient Object Detection [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2025 . |
| MLA | Yao, Zhaojian et al. "Collaborating Constrained and Unconstrained Encodings for Cross-Modal Salient Object Detection" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2025) . |
| APA | Yao, Zhaojian , Gao, Wei , Li, Ge , Zhao, Tiesong . Collaborating Constrained and Unconstrained Encodings for Cross-Modal Salient Object Detection . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2025 . |
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Image enhancement methods have been widely studied to improve the visual quality of diverse images, implicitly assuming that all human observers have normal vision. However, a large population around the world suffers from Color Vision Deficiency (CVD). Enhancing images to compensate for their perceptions remains a challenging issue. Existing CVD compensation methods have two drawbacks: first, the available datasets and validations have not been rigorously tested by CVD individuals; second, these methods struggle to strike an optimal balance between contrast enhancement and naturalness preservation, which often results in suboptimal outcomes for individuals with CVD. To address these issues, we develop the first large-scale, CVD-individual-labeled dataset called FZU-CVDSet and a CVD-friendly recoloring algorithm called ColorAssist. In particular, we design a perception-guided feature extraction module and a perception-guided diffusion transformer module that jointly achieve efficient image recoloring for individuals with CVD. Comprehensive experiments on both FZU-CVDSet and subjective tests in hospitals demonstrate that the proposed ColorAssist closely aligns with the visual perceptions of individuals with CVD, achieving superior performance compared with the state-of-the-arts. The source code is available at https://github.com/xsx-fzu/ColorAssist.
Keyword ：
color vision deficiency (CVD) color vision deficiency (CVD) Image enhancement Image enhancement recoloring recoloring visual perception visual perception
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| GB/T 7714 | Lin, Liqun , Xie, Shangxi , Wang, Yanting et al. ColorAssist: Perception-Based Recoloring for Color Vision Deficiency Compensation [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2025 , 34 : 5658-5671 . |
| MLA | Lin, Liqun et al. "ColorAssist: Perception-Based Recoloring for Color Vision Deficiency Compensation" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 34 (2025) : 5658-5671 . |
| APA | Lin, Liqun , Xie, Shangxi , Wang, Yanting , Chen, Bolin , Xue, Ying , Zhuang, Xiahai et al. ColorAssist: Perception-Based Recoloring for Color Vision Deficiency Compensation . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2025 , 34 , 5658-5671 . |
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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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Being able to estimate monocular depth for spherical panoramas is of fundamental importance in 3D scene perception. However, spherical distortion severely limits the effectiveness of vanilla convolutions. To push the envelope of accuracy, recent approaches attempt to utilize Tangent projection (TP) to estimate the depth of 360(degrees) images. Yet, these methods still suffer from discrepancies and inconsistencies among patch-wise tangent images, as well as the lack of accurate ground truth depth maps under a supervised fashion. In this paper, we propose a geometry-aware self-supervised 360(degrees) image depth estimation methodology that explores the complementary advantages of TP and Equirectangular projection (ERP) by an asymmetric dual-domain collaborative learning strategy. Especially, we first develop a lightweight asymmetric dual-domain depth estimation network, which enables to aggregate depth-related features from a single TP domain, and then produce depth distributions of the TP and ERP domains via collaborative learning. This effectively mitigates stitching artifacts and preserves fine details in depth inference without overspending model parameters. In addition, a frequent-spatial feature concentration module is devised to simultaneously capture non-local Fourier features and local spatial features, such that facilitating the efficient exploration of monocular depth cues. Moreover, we introduce a geometric structural alignment module to further improve geometric structural consistency among tangent images. Extensive experiments illustrate that our designed approach outperforms existing self-supervised 360(degrees) depth estimation methods on three publicly available benchmark datasets.
Keyword ：
360(degrees) image 360(degrees) image Accuracy Accuracy depth estimation depth estimation Depth measurement Depth measurement Distortion Distortion Estimation Estimation Feature extraction Feature extraction Federated learning Federated learning Image reconstruction Image reconstruction self-supervised learning self-supervised learning tangent projection tangent projection Three-dimensional displays Three-dimensional displays Transformers Transformers Visualization Visualization
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| GB/T 7714 | Wang, Xu , He, Ziyan , Zhang, Qiudan et al. Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 : 3224-3237 . |
| MLA | Wang, Xu et al. "Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning" . | IEEE TRANSACTIONS ON MULTIMEDIA 27 (2025) : 3224-3237 . |
| APA | Wang, Xu , He, Ziyan , Zhang, Qiudan , Yang, You , Zhao, Tiesong , Jiang, Jianmin . Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning . | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 , 3224-3237 . |
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Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://github.com/AlchemyEmperor/SIAVC.
Keyword ：
consistency regularization consistency regularization deep learning deep learning distribution alignment distribution alignment Video classification Video classification
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| GB/T 7714 | Li, Zuoyong , Lin, Qinghua , Fan, Haoyi et al. SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (3) : 2603-2615 . |
| MLA | Li, Zuoyong et al. "SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 3 (2025) : 2603-2615 . |
| APA | Li, Zuoyong , Lin, Qinghua , Fan, Haoyi , Zhao, Tiesong , Zhang, David . SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (3) , 2603-2615 . |
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In recent decades, the emergence of image applications has greatly facilitated the development of vision-based tasks. As a result, image quality assessment (IQA) has become increasingly significant for monitoring, controlling, and improving visual signal quality. While existing IQA methods focus on image fidelity and aesthetics to characterize perceived quality, it is important to evaluate the utility-centered quality of an image for popular tasks, such as object detection. However, research shows that there is a low correlation between utilities and perceptions. To address this issue, this article proposes a utility-centered IQA approach. Specifically, our research focuses on underwater fish detection as a challenging task in an underwater environment. Based on this task, we have developed a utility-centered underwater image quality database (UIQD) and a transfer learning-based advanced underwater quality by utility assessment (AQUA). Inspired by the top-down design approach used in fidelity-oriented IQA methods, we utilize deep models of object detection and transfer their features to the mission of utility-centered quality evaluation. Experimental results validate that the proposed AQUA achieves promising performance not only in fish detection but also in other tasks such as face recognition. We believe that our research provides valuable insights to bridge the gap between IQA research and visual tasks.
Keyword ：
Convolutional neural networks Convolutional neural networks Databases Databases Feature extraction Feature extraction Image color analysis Image color analysis Image quality Image quality Image quality assessment (IQA) Image quality assessment (IQA) Neck Neck Quality assessment Quality assessment Training Training Transformers Transformers underwater images underwater images utility-centered IQA utility-centered IQA YOLO YOLO
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| GB/T 7714 | Chen, Weiling , Liao, Honggang , Lin, Rongfu et al. Utility-Centered Underwater Image Quality Evaluation [J]. | IEEE JOURNAL OF OCEANIC ENGINEERING , 2025 , 50 (2) : 743-757 . |
| MLA | Chen, Weiling et al. "Utility-Centered Underwater Image Quality Evaluation" . | IEEE JOURNAL OF OCEANIC ENGINEERING 50 . 2 (2025) : 743-757 . |
| APA | Chen, Weiling , Liao, Honggang , Lin, Rongfu , Zhao, Tiesong , Gu, Ke , Le Callet, Patrick . Utility-Centered Underwater Image Quality Evaluation . | IEEE JOURNAL OF OCEANIC ENGINEERING , 2025 , 50 (2) , 743-757 . |
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Sonar technology has been widely used in underwater surface mapping and remote object detection for its light-independent characteristics. Recently, the booming of artificial intelligence further surges sonar image (SI) processing and understanding techniques. However, the intricate marine environments and diverse nonlinear postprocessing operations may degrade the quality of SIs, impeding accurate interpretation of underwater information. Efficient image quality assessment (IQA) methods are crucial for quality monitoring in sonar imaging and processing. Existing IQA methods overlook the unique characteristics of SIs or focus solely on typical distortions in specific scenarios, which limits their generalization capability. In this article, we propose a unified sonar IQA method, which overcomes the challenges posed by diverse distortions. Though degradation conditions are changeable, ideal SIs consistently require certain properties that must be task-centered and exhibit attribute consistency. We derive a comprehensive set of quality attributes from both the task background and visual content of SIs. These attribute features are represented in just ten dimensions and ultimately mapped to the quality score. To validate the effectiveness of our method, we construct the first comprehensive SI dataset. Experimental results demonstrate the superior performance and robustness of the proposed method.
Keyword ：
Attribute consistency Attribute consistency Degradation Degradation Distortion Distortion Image quality Image quality image quality assessment (IQA) image quality assessment (IQA) Imaging Imaging Noise Noise Nonlinear distortion Nonlinear distortion no-reference (NR) no-reference (NR) Quality assessment Quality assessment Silicon Silicon Sonar Sonar sonar imaging and processing sonar imaging and processing Sonar measurements Sonar measurements
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| GB/T 7714 | Cai, Boqin , Chen, Weiling , Zhang, Jianghe et al. Unified No-Reference Quality Assessment for Sonar Imaging and Processing [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Cai, Boqin et al. "Unified No-Reference Quality Assessment for Sonar Imaging and Processing" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Cai, Boqin , Chen, Weiling , Zhang, Jianghe , Junejo, Naveed Ur Rehman , Zhao, Tiesong . Unified No-Reference Quality Assessment for Sonar Imaging and Processing . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Due to the complex underwater imaging environment, existing Underwater Image Enhancement (UIE) techniques are unable to handle the increasing demand for high-quality underwater content in broadcasting systems. Thus, a robust quality assessment method is highly expected to effectively compare the quality of different enhanced underwater images. To this end, we propose a novel quality assessment method for enhanced underwater images by utilizing multiple levels of features at various stages of the network's depth. We first select underwater images with different distortions to analyze the characteristics of different UIE results at various feature levels. We found that low-level features are more sensitive to color information, while mid-level features are more indicative of structural differences. Based on this, a Channel-Spatial-Pixel Attention Module (CSPAM) is designed for low-level perception to capture color characteristics, utilizing channel, spatial, and pixel dimensions. To capture structural variations, a Parallel Structural Perception Module (PSPM) with convolutional kernels of different scales is introduced for mid-level perception. For high-level perception, due to the accumulation of noise, an Adaptive Weighted Downsampling (AWD) layer is employed to restore the semantic information. Furthermore, a new top-down multi-level feature fusion method is designed. Information from different levels is integrated through a Selective Feature Fusion (SFF) mechanism, which produces semantically rich features and enhances the model's feature representation capability. Experimental results demonstrate the superior performance of the proposed method over the competing image quality evaluation methods.
Keyword ：
image quality assessment image quality assessment multi-level perception multi-level perception Underwater image enhancement Underwater image enhancement
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| GB/T 7714 | Xu, Yiwen , Lin, Yuxiang , He, Nian et al. Multi-Level Perception Assessment for Underwater Image Enhancement [J]. | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) : 606-615 . |
| MLA | Xu, Yiwen et al. "Multi-Level Perception Assessment for Underwater Image Enhancement" . | IEEE TRANSACTIONS ON BROADCASTING 71 . 2 (2025) : 606-615 . |
| APA | Xu, Yiwen , Lin, Yuxiang , He, Nian , Wang, Xuejin , Zhao, Tiesong . Multi-Level Perception Assessment for Underwater Image Enhancement . | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) , 606-615 . |
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Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained network is applied to more detailed X-rays, differences of similarity can limit the robustness of these feature representations. Therefore, we propose an intra- and inter-correlation learning framework for chest X-ray anomaly detection. Firstly, to better leverage the similar anatomical structure information in chest X-rays, we introduce the Anatomical-Feature Pyramid Fusion Module for feature fusion. This module aims to obtain fusion features with both local details and global contextual information. These fusion features are initialized by a trainable feature mapper and stored in a feature bank to serve as centers for learning. Furthermore, to Facing Differences of Similarity (FDS) introduced by the pre-trained network, we propose an intra- and inter-correlation learning strategy: 1) We use intra-correlation learning to establish intra-correlation between mapped features of individual images and semantic centers, thereby initially discovering lesions; 2) We employ inter-correlation learning to establish inter-correlation between mapped features of different images, further mitigating the differences of similarity introduced by the pre-trained network, and achieving effective detection results even in diverse chest disease environments. Finally, a comparison with 18 state-of-the-art methods on three datasets demonstrates the superiority and effectiveness of the proposed method across various scenarios.
Keyword ：
chest X-ray chest X-ray correlation learning correlation learning feature fusion feature fusion Medical anomaly detection Medical anomaly detection transfer learning transfer learning
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| GB/T 7714 | Xu, Shicheng , Li, Wei , Li, Zuoyong et al. Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2025 , 44 (2) : 801-814 . |
| MLA | Xu, Shicheng et al. "Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 44 . 2 (2025) : 801-814 . |
| APA | Xu, Shicheng , Li, Wei , Li, Zuoyong , Zhao, Tiesong , Zhang, Bob . Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2025 , 44 (2) , 801-814 . |
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