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学者姓名：魏宏安
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Abstract ：
Rain streaks typically cause significant visual degradation and foreground occlusions, hindering the progress of visual tasks in outdoor scenarios. Existing image deraining methods, predominantly based on Convolutional Neural Networks (CNNs), exhibit certain limitations. These methods tend to overly focus on low-level visual features, demonstrating insufficient ability to capture high-dimensional global features. Furthermore, they often lack targeted attention to channel information and spatial details, which restricts their effectiveness. To address these shortcomings, this paper proposes the Delta-Calibration Derain Network (DCD-Net). The DCD-Net introduces a sequential Delta Convolutional Layer structure to significantly expand the feature acquisition range. Additionally, this study pioneers the Joint Calibration Attention module, which precisely captures both channel and spatial feature information, leading to enhanced network performance. Experimental results across multiple synthetic datasets show that the proposed method achieves superior performance in terms of Peak Signal-to-Noise Ratio and Structural Similarity Index, validating the advantages of DCD-Net over traditional CNN-based models.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Image deraining Image deraining Image processing Image processing
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| GB/T 7714 | Que, Hanjing , Weng, Jianing , Fang, Ying et al. DCD-Net: image deraining with delta convolution and joint calibration attention [J]. | SIGNAL IMAGE AND VIDEO PROCESSING , 2025 , 19 (1) . |
| MLA | Que, Hanjing et al. "DCD-Net: image deraining with delta convolution and joint calibration attention" . | SIGNAL IMAGE AND VIDEO PROCESSING 19 . 1 (2025) . |
| APA | Que, Hanjing , Weng, Jianing , Fang, Ying , Hu, Kejian , Wei, Hongan , Xu, Yiwen . DCD-Net: image deraining with delta convolution and joint calibration attention . | SIGNAL IMAGE AND VIDEO PROCESSING , 2025 , 19 (1) . |
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The recent advances in multimedia technology have significantly expanded the range of audio-visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion and widespread availability. Within media content, the video signals are presented in various formats including stereoscopic/3D, panoramic/360 degrees degrees and holographic images. The signals are also combined with other sensory elements, such as audio, tactile, and olfactory cues, creating a comprehensive multi-sensory experience for the user. The development of both qualitative and quantitative Quality of Experience (QoE) metrics is crucial for enhancing the subjective experience in immersive scenarios, providing valuable guidelines for system enhancement. In this paper, we review the most recent achievements in QoE assessment for immersive scenarios, summarize the current challenges related to QoE issues, and present outlooks of QoE applications in these scenarios. The aim of our overview is to offer a valuable reference for researchers in the domain of multimedia delivery.
Keyword ：
Immersive video Immersive video MULtiple SEnsorial MEDIA (MULSEMEDIA) MULtiple SEnsorial MEDIA (MULSEMEDIA) Quality of Experience (QoE) Quality of Experience (QoE) Video delivery Video delivery Video transmission Video transmission
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| GB/T 7714 | Chen, Weiling , Lan, Fengquan , Wei, Hongan et al. A comprehensive review of quality of experience for emerging video services [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
| MLA | Chen, Weiling et al. "A comprehensive review of quality of experience for emerging video services" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 128 (2024) . |
| APA | Chen, Weiling , Lan, Fengquan , Wei, Hongan , Zhao, Tiesong , Liu, Wei , Xu, Yiwen . A comprehensive review of quality of experience for emerging video services . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
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Sonar sensors are vital in the marine industry for detecting underwater targets in challenging conditions. The imaging distance and image resolution are negatively correlated due to the propagation characteristics of sound waves in water. Although Super -Resolution (SR) techniques alleviate this limitation, they introduce complex distortions that may not fit the desired utility of reconstructed sonar images. Quantifying image quality is essential, yet existing Image Quality Assessment (IQA) algorithms fail to simultaneously consider reconstruction distortions and the sonar image task background. Furthermore, the scarcity of sonar images poses challenges for deep -learning -based algorithms. To address these issues, we propose a brain -inspired model for SuperResolution reconstructed Sonar Image Quality Assessment (SRSIQA) based on transfer learning. On the one hand, we adopt an effective feature extractor to extract multi -level features that align with the human brain recognition process. On the other hand, we develop an SEA -Block to implement feature weight adjustment and multi -level feature scale matching, given that some transferred features do not fit the IQA task well. Experimental results demonstrate the superiority of the proposed method.
Keyword ：
Hierarchical representation Hierarchical representation Image quality assessment Image quality assessment Super-resolution reconstructed sonar images Super-resolution reconstructed sonar images Transfer learning Transfer learning
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| GB/T 7714 | Feng, Qianxue , Zheng, Sumei , Zhang, Keke et al. A brain-inspired quality assessment model for sonar image super-resolution [J]. | DISPLAYS , 2024 , 82 . |
| MLA | Feng, Qianxue et al. "A brain-inspired quality assessment model for sonar image super-resolution" . | DISPLAYS 82 (2024) . |
| APA | Feng, Qianxue , Zheng, Sumei , Zhang, Keke , Wei, Hongan . A brain-inspired quality assessment model for sonar image super-resolution . | DISPLAYS , 2024 , 82 . |
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Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization.
Keyword ：
Image quality assessment Image quality assessment Underwater image datasets Underwater image datasets Underwater image enhancement Underwater image enhancement Underwater image restoration Underwater image restoration
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| GB/T 7714 | Wang, Mingjie , Zhang, Keke , Wei, Hongan et al. Underwater image quality optimization: Researches, challenges, and future trends [J]. | IMAGE AND VISION COMPUTING , 2024 , 146 . |
| MLA | Wang, Mingjie et al. "Underwater image quality optimization: Researches, challenges, and future trends" . | IMAGE AND VISION COMPUTING 146 (2024) . |
| APA | Wang, Mingjie , Zhang, Keke , Wei, Hongan , Chen, Weiling , Zhao, Tiesong . Underwater image quality optimization: Researches, challenges, and future trends . | IMAGE AND VISION COMPUTING , 2024 , 146 . |
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Underwater images often suffer from local distortions during the imaging and transmission process, which can negatively impact their quality. Fortunately, it is possible to improve image quality by removing local distortion without making any hardware or software adjustments to the transmission system. However, existing algorithms designed for global distortions are not suitable for addressing local distortions, while end -to -end restoration and inpainting algorithms do not perform satisfactorily on underwater images. To address this issue, this paper proposes a Joint distortion localization and restoration model based on Progressive Guidance (JPG) specifically tailored for underwater imaging and transmission. Our strategy employs a two -stage framework where the first stage focuses exclusively on accurately localizing distortions to obtain precise position. Subsequently, in the second stage, we utilize this position information for effective distortion restoration. To further enhance restoration performance, our approach progressively guides the restoration process by incorporating global, distortion -free as well as distortion -specific information into different components of the second -stage network. The work surpasses current state-of-the-art methods in restoring both mixed and individual distortions.
Keyword ：
Distortion localization Distortion localization Progressive guidance Progressive guidance Underwater image restoration Underwater image restoration Underwater local distortion Underwater local distortion
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| GB/T 7714 | Zhang, Jianghe , Chen, Weiling , Lin, Zuxin et al. Underwater image restoration based on progressive guidance [J]. | SIGNAL PROCESSING , 2024 , 223 . |
| MLA | Zhang, Jianghe et al. "Underwater image restoration based on progressive guidance" . | SIGNAL PROCESSING 223 (2024) . |
| APA | Zhang, Jianghe , Chen, Weiling , Lin, Zuxin , Wei, Hongan , Zhao, Tiesong . Underwater image restoration based on progressive guidance . | SIGNAL PROCESSING , 2024 , 223 . |
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The limited bandwidth of underwater acoustic channels poses a challenge to the efficiency of multimedia information transmission. To improve efficiency, the system aims to transmit less data while maintaining image utility at the receiving end. Although assessing utility within compressed information is essential, the current methods exhibit limitations in addressing utility-driven quality assessment. Therefore, this letter built a Utility-oriented compacted Image Quality Dataset (UCIQD) that contains utility qualities of reference images and their corresponding compcated information at different levels. The utility score is derived from the average confidence of various object detection models. Then, based on UCIQD, we introduce a Distillation-based Compacted Information Quality assessment metric (DCIQ) for utility-oriented quality evaluation in the context of underwater machine vision. In DCIQ, utility features of compacted information are acquired through transfer learning and mapped using a Transformer. Besides, we propose a utility-oriented cross-model feature fusion mechanism to address different detection algorithm preferences. After that, a utility-oriented feature quality measure assesses compacted feature utility. Finally, we utilize distillation to compress the model by reducing its parameters by 55%. Experiment results effectively demonstrate that our proposed DCIQ can predict utility-oriented quality within compressed underwater information.
Keyword ：
Compacted underwater information Compacted underwater information distillation distillation utility-oriented quality assessment utility-oriented quality assessment
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| GB/T 7714 | Liao, Honggang , Jiang, Nanfeng , Chen, Weiling et al. Distillation-Based Utility Assessment for Compacted Underwater Information [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 481-485 . |
| MLA | Liao, Honggang et al. "Distillation-Based Utility Assessment for Compacted Underwater Information" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 481-485 . |
| APA | Liao, Honggang , Jiang, Nanfeng , Chen, Weiling , Wei, Hongan , Zhao, Tiesong . Distillation-Based Utility Assessment for Compacted Underwater Information . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 481-485 . |
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With the burgeoning growth of the e-sports industry, there has been a rapid proliferation of gaming videos on online platforms. Simultaneously, this surge undoubtedly presents significant challenges to the encoding of game videos. However, the recurring characteristic of gaming videos is not efficiently studied in the current standardization, such as Versatile Video Coding (VVC). Based on these observations, our general framework, utilizing the Structural SIMilarity index metric (SSIM), Hash SIMilarity index metric (HSIM), and Hash Matrix SIMilarity index metric (HMSIM), identifies recurring video clips and adjusts the reference frame of the first frame. Additionally, we analyze recurring patterns in different resolutions and types, proposing our Scene Adaptation (SA) optimization algorithm, which integrates SSIM, HSIM, and HMSIM to adapt to various resolutions and scenes. Experimental results show that the proposed approach can achieve a -4.71 % Bjontegaard Delta Bit Rate (BDBR) and 1.00% Time Save (TS), outperforming the benchmark. © 2024 IEEE.
Keyword ：
component component Gaming Video Coding Gaming Video Coding Recurring Characteristic Recurring Characteristic Scene Adaptation Scene Adaptation Versatile Video Coding Versatile Video Coding
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| GB/T 7714 | Lin, C. , Lin, S. , Fang, Y. et al. Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding [未知]. |
| MLA | Lin, C. et al. "Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding" [未知]. |
| APA | Lin, C. , Lin, S. , Fang, Y. , Yi, S. , Wei, H. , Xu, Y. . Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding [未知]. |
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水下观测是探索海洋最直观的手段之一。受水下光学特性、声学特性以及杂波、水生生物等的影响，水下观测中所采集的图像并不总能满足观测需求。如何对水下图像进行有效的处理、分析与应用是一个具有挑战性的课题。尽管图像处理与计算机视觉技术已在大气环境中得到广泛研究，但鉴于成像原理、应用背景等方面的差异，针对大气自然图像提出的算法无法直接移植到水下任务中，而针对水下场景提出的视觉应用仍存在对任务背景考虑不足、泛化性差等缺陷。本文面向光学图像以及声学图像这两类水下观测的主要手段，从图像特性入手，首次以任务为导向、以需求为脉络，通过梳理国内外成功的水下图像处理、质量评价案例，对水下观测方案的工作思路进行了更完备的总结与分析。此外，本文围绕水下机器视觉应用探讨其发展进程，详细讨论与展望了相关领域的前景与优化方向，为突破海洋视觉应用的瓶颈，建设智慧海洋系统带来新思路。
Keyword ：
智慧海洋 智慧海洋 机器视觉 机器视觉 水下图像处理 水下图像处理 质量评价 质量评价
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| GB/T 7714 | 陈炜玲 , 邱艳玲 , 赵铁松 et al. 面向海洋的水下图像处理与视觉技术进展 [J]. | 信号处理 , 2023 , 39 (10) : 1748-1763 . |
| MLA | 陈炜玲 et al. "面向海洋的水下图像处理与视觉技术进展" . | 信号处理 39 . 10 (2023) : 1748-1763 . |
| APA | 陈炜玲 , 邱艳玲 , 赵铁松 , 魏宏安 , 程恩 . 面向海洋的水下图像处理与视觉技术进展 . | 信号处理 , 2023 , 39 (10) , 1748-1763 . |
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To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Image enhancement Image enhancement Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Qiu, Yanling , Feng, Qianxue , Cai, Boqin et al. A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection [C] . 2023 : 203-216 . |
| MLA | Qiu, Yanling et al. "A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection" . (2023) : 203-216 . |
| APA | Qiu, Yanling , Feng, Qianxue , Cai, Boqin , Wei, Hongan , Chen, Weiling . A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection . (2023) : 203-216 . |
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No-Reference (NR) quality assessment is a crucial approach for evaluating the quality of low-light enhanced images, as it is often difficult to acquire high-quality reference images in applications such as night-time automatic driving. However, current NR evaluation methods for low-light enhanced images often lack consideration of important characteristics such as color, structure, and naturalness. This paper proposes a novel NR quality assessment method for NR low-light enhanced images from both subjective and objective aspects. On the subjective side, we construct the Low-light Enhanced Images Subjective Dataset (LEISD) containing 2040 images with 255 different image contents. Each image was evaluated based on the Single Stimulus (SS) method by 20 subjects. On the objective side, we propose Multi-Features Reconciliation-based Quality Assessment (MFRQA) methods for low-light enhanced images by observing the low-light enhanced images. The MFRQA summarized four key feature perspectives: brightness, color, structure and naturalness, and employed the traditional machine learning model to reconcile the multi-features. Experimental results on the LEISD dataset demonstrate competitive performance and low complexity of our method compared to the representative quality metrics.
Keyword ：
Blind Blind Feature reconciliation Feature reconciliation Image quality assessment(IQA) Image quality assessment(IQA) Low-light enhanced image Low-light enhanced image no-reference(NR) no-reference(NR)
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| GB/T 7714 | Lin, Weitao , Wu, Yuxuan , Xu, Lishi et al. No-reference quality assessment for low-light image enhancement: Subjective and objective methods [J]. | DISPLAYS , 2023 , 78 . |
| MLA | Lin, Weitao et al. "No-reference quality assessment for low-light image enhancement: Subjective and objective methods" . | DISPLAYS 78 (2023) . |
| APA | Lin, Weitao , Wu, Yuxuan , Xu, Lishi , Chen, Weiling , Zhao, Tiesong , Wei, Hongan . No-reference quality assessment for low-light image enhancement: Subjective and objective methods . | DISPLAYS , 2023 , 78 . |
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