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学者姓名：魏宏安
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Abstract ：
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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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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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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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 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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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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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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高空抛物引发的人身财产损失案件频发，并且抛物行为迅速而突然，目击者少且不稳定，这导致了案件的取证工作变得极为复杂.针对此类现象，提出一种基于边缘计算的鱼眼相机高空抛物检测系统，主要分为图像视频采集、终端动目标检测以及边缘端目标识别3个模块，在终端将高空抛物视频矫正畸变后，利用三帧差法对抛物者与抛物对象进行标记，随后发送视频序列至边缘端进行目标识别.实验结果的平均精确率均值为0.994,结果表明该系统能够有效检测高空抛物行为.
Keyword ：
棋盘格标定 棋盘格标定 目标检测 目标检测 边缘计算 边缘计算 鱼眼相机 鱼眼相机
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| GB/T 7714 | 林诗妍 , 刘宇翔 , 魏宏安 . 基于边缘计算的鱼眼相机高空抛物检测系统 [J]. | 福建师范大学学报(自然科学版) , 2023 , 39 (06) : 52-62 . |
| MLA | 林诗妍 et al. "基于边缘计算的鱼眼相机高空抛物检测系统" . | 福建师范大学学报(自然科学版) 39 . 06 (2023) : 52-62 . |
| APA | 林诗妍 , 刘宇翔 , 魏宏安 . 基于边缘计算的鱼眼相机高空抛物检测系统 . | 福建师范大学学报(自然科学版) , 2023 , 39 (06) , 52-62 . |
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As a powerful machine learning technique, deep learning has been widely applied to lesion detection in medical image processing. This review summarizes the research progress of deep learning applications in lesion detection. Firstly, the characteristics of medical image data are introduced, and the datasets and evaluation metrics of lesion detection are summarized. Then, the main contents of deep learning, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), YOLO algorithm, and SAM, have demonstrated good performance in medical image processing. Meanwhile, the applications of lesion detection in different medical image modalities are discussed, and the advantages of deep learning in different lesion types are highlighted, such as high automation, good performance, and transferability. In addition, some challenges of deep learning in lesion detection are discussed, such as sample scarcity, interpretability, and reliability. Finally, the future development directions of deep learning in lesion detection are discussed, such as multimodal fusion, transfer learning, and labeled data. This review provides a comprehensive overview of the research progress of deep learning in the field of lesion detection, which offers guidance and reference for related research and applications. © 2023 IEEE.
Keyword ：
Convolutional neural networks Convolutional neural networks Generative adversarial networks Generative adversarial networks Learning systems Learning systems Medical imaging Medical imaging Recurrent neural networks Recurrent neural networks Transfer learning Transfer learning
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| GB/T 7714 | Chen, Tao , Geng, Yi , Li, Lanlan et al. A Review of Deep Learning Applications in Lesion Detection Research [C] . 2023 : 181-188 . |
| MLA | Chen, Tao et al. "A Review of Deep Learning Applications in Lesion Detection Research" . (2023) : 181-188 . |
| APA | Chen, Tao , Geng, Yi , Li, Lanlan , Wei, Hongan . A Review of Deep Learning Applications in Lesion Detection Research . (2023) : 181-188 . |
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