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学者姓名：赵铁松
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Abstract ：
Hairstyle transfer is challenging due to intricate nature of hairstyles. In particular, face misalignment leads to distortion or deformation of the transferred hairstyle. To address this issue, we propose a Robust and Efficient Hairstyle transfer (REHair) framework, which comprises three stages: adaptive angle alignment, adaptive depth alignment, and efficient hairstyle editing. Firstly, we perform head pose estimation and adjust the facial rotation angle based on the latent code, thus ensuring consistent facial orientation between the face image and the hairstyle reference image and preventing hair shape and texture loss from iterative optimization methods. Secondly, we employ monocular depth estimation to predict the face depth of both images and perform adaptive depth alignment, ensuring the preservation of more hairstyle details. Finally, we propose a fast image embedding algorithm and integrate it with the latent code, significantly reducing the image embedding time in StyleGAN2. This adaptation enables REHair to be suitable for real-time applications. Quantitative and qualitative evaluations on the FFHQ and CelebA-HQ dataset demonstrate that REHair achieves state-of-the-art performance by successfully transferring hairstyles between images with different poses. The proposed method significantly reduces image embedding time while preserving image quality, and effectively addresses challenges associated with sub-optimal photography conditions and slow generation speed. Source code avaliable at https://github.com/fdwxfy/REHair.
Keyword ：
Adaptive angle alignment Adaptive angle alignment Adaptive depth alignment Adaptive depth alignment Face misalignment Face misalignment Fast image embedding Fast image embedding Hairstyle transfer Hairstyle transfer
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| GB/T 7714 | Xu, Yiwen , Ling, Liping , Lin, Qingxu et al. REHair: Efficient hairstyle transfer robust to face misalignment [J]. | PATTERN RECOGNITION , 2025 , 164 . |
| MLA | Xu, Yiwen et al. "REHair: Efficient hairstyle transfer robust to face misalignment" . | PATTERN RECOGNITION 164 (2025) . |
| APA | Xu, Yiwen , Ling, Liping , Lin, Qingxu , Fang, Ying , Zhao, Tiesong . REHair: Efficient hairstyle transfer robust to face misalignment . | PATTERN RECOGNITION , 2025 , 164 . |
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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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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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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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The introduction of multiple viewpoints in video scenes inevitably increases the bitrates required for storage and transmission. To reduce bitrates, researchers have developed methods to skip intermediate viewpoints during compression and delivery, and ultimately reconstruct them using Side Information (SInfo). Typically, depth maps are used to construct SInfo. However, these methods suffer from reconstruction inaccuracies and inherently high bitrates. In this paper, we propose a novel multi-view video coding method that leverages the image generation capabilities of Generative Adversarial Network (GAN) to improve the reconstruction accuracy of SInfo. Additionally, we consider incorporating information from adjacent temporal and spatial viewpoints to further reduce SInfo redundancy. At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize a convolutional network to extract the latent code of a GAN as SInfo. At the decoder, we combine the SInfo and adjacent viewpoints to reconstruct intermediate views using the GAN generator. Specifically, we establish a joint encoder constraint for reconstruction cost and SInfo entropy to achieve an optimal trade-off between reconstruction quality and bitrate overhead. Experiments demonstrate the significant improvement in Rate-Distortion (RD) performance compared to state-of-the-art methods.
Keyword ：
Epipolar plane image Epipolar plane image Generative adversarial network Generative adversarial network Latent code learning Latent code learning Multi-view video coding Multi-view video coding
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| GB/T 7714 | Lan, Chengdong , Yan, Hao , Luo, Cheng et al. GAN-based multi-view video coding with spatio-temporal EPI reconstruction [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2025 , 132 . |
| MLA | Lan, Chengdong et al. "GAN-based multi-view video coding with spatio-temporal EPI reconstruction" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 132 (2025) . |
| APA | Lan, Chengdong , Yan, Hao , Luo, Cheng , Zhao, Tiesong . GAN-based multi-view video coding with spatio-temporal EPI reconstruction . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2025 , 132 . |
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Video compression artifact removal focuses on enhancing the visual quality of compressed videos by mitigating visual distortions. However, existing methods often struggle to effectively capture spatio-temporal features and recover high-frequency details, due to their suboptimal adaptation to the characteristics of compression artifacts. To overcome these limitations, we propose a novel Spatio-Temporal and Frequency Fusion (STFF) framework. STFF incorporates three key components: Feature Extraction and Alignment (FEA), which employs SRU for effective spatiotemporal feature extraction; Bidirectional High-Frequency Enhanced Propagation (BHFEP), which integrates HCAB to restore high-frequency details through bidirectional propagation; and Residual High-Frequency Refinement (RHFR), which further enhances high-frequency information. Extensive experiments demonstrate that STFF achieves superior performance compared to state-of-the-art methods in both objective metrics and subjective visual quality, effectively addressing the challenges posed by video compression artifacts. Trained model available: https://github.com/Stars-WMX/STFF.
Keyword ：
Degradation Degradation Feature extraction Feature extraction Image coding Image coding Image restoration Image restoration Motion compensation Motion compensation Optical flow Optical flow Quality assessment Quality assessment Spatiotemporal phenomena Spatiotemporal phenomena Transformers Transformers video coding video coding Video compression Video compression Video compression artifact removal Video compression artifact removal video enhancement video enhancement video quality video quality
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| GB/T 7714 | Wang, Mingxing , Liao, Yipeng , Chen, Weiling et al. STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal [J]. | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) : 542-554 . |
| MLA | Wang, Mingxing et al. "STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal" . | IEEE TRANSACTIONS ON BROADCASTING 71 . 2 (2025) : 542-554 . |
| APA | Wang, Mingxing , Liao, Yipeng , Chen, Weiling , Lin, Liqun , Zhao, Tiesong . STFF: Spatio-Temporal and Frequency Fusion for Video Compression Artifact Removal . | IEEE TRANSACTIONS ON BROADCASTING , 2025 , 71 (2) , 542-554 . |
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Owing to the rapid development of emerging 360(degrees)panoramic imaging techniques, indoor 360(degrees)depth estimation has aroused extensive attention in the community. Due to the lack of available ground truth depth data, it is extremely urgent to model indoor 360(degrees)depth estimation in self-supervised mode. However, self-supervised 360 degrees depth estimation suffers from two major limitations. One is the distortion and network training problems caused by Equirectangular projection (ERP), and the other is that texture-less regions are quite difficult to back-propagate in self-supervised mode. Hence, to address the above issues, we introduce spherical view synthesis for learning self-supervised 360(degrees)depthestimation. Specifically, to alleviate the ERP-related problems, we first propose a dual-branch distortion-aware network to produce the coarse depth map, including a distortion-aware module and a hybrid projection fusion module. Subsequently, the coarse depth map is utilized for spherical view synthesis, in which a spherically weighted loss function for view reconstruction and depth smoothing is investigated to optimize the projection distribution problem of360(degrees)images. In addition, two structural regularities of indoor360(degrees)scenes are devised as two additional supervisory signals to efficiently optimize our self-supervised 360(degrees)depth estimation model, containing the principal-direction normal constraint and the co-planar depth constraint. The principal-direction normal constraint is designed to align the normal of the 360(degrees)imagewith the direction of the vanishing points. Meanwhile, we employ the co-planar depth constraint to fit the estimated depth of each pixel through its 3D plane. Finally, a depth map is obtained for the 360(degrees)image. Experimental results illustrate that our proposed method achieves superior performance than the current advanced depth estimation methods on four publicly available datasets
Keyword ：
360(degrees) image 360(degrees) image depth estimation depth estimation Distortion Distortion Estimation Estimation Feature extraction Feature extraction Image reconstruction Image reconstruction self-supervised learning self-supervised learning Self-supervised learning Self-supervised learning structural regularity structural regularity Task analysis Task analysis Training Training
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| GB/T 7714 | Wang, Xu , Kong, Weifeng , Zhang, Qiudan et al. Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 3998-4011 . |
| MLA | Wang, Xu et al. "Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 3998-4011 . |
| APA | Wang, Xu , Kong, Weifeng , Zhang, Qiudan , Yang, You , Zhao, Tiesong , Jiang, Jianmin . Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 3998-4011 . |
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The Just Noticeable Difference (JND) model aims to identify perceptual redundancies in images by simulating the perception of the Human Visual System (HVS). Exploring the JND of sonar images is important for the study of their visual properties and related applications. However, there is still room for improvement in performance of existing JND models designed for Natural Scene Images (NSIs), and the characteristics of sonar images are not sufficiently considered by them. On the other hand, there are significant challenges in constructing a densely labeled pixel-level JND dataset. To tackle these issues, we proposed a pixel-level JND model based on inexact supervised learning. A perceptually lossy/lossless predictor was first pre-trained on a coarsegrained picture-level JND dataset. This predictor can guide the unsupervised generator to produce an image that is perceptually lossless compared to the original image. Then we designed a loss function to ensure that the generated image is perceptually lossless and maximally different from the original image. Experimental results show that our model outperforms current models.
Keyword ：
Inexact Supervised Learning Inexact Supervised Learning Just Noticeable Difference (JND) Just Noticeable Difference (JND) Sonar Images Sonar Images
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| GB/T 7714 | Feng, Qianxue , Wang, Mingjie , Chen, Weiling et al. Pixel-Level Sonar Image JND Based on Inexact Supervised Learning [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI , 2024 , 14435 : 469-481 . |
| MLA | Feng, Qianxue et al. "Pixel-Level Sonar Image JND Based on Inexact Supervised Learning" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI 14435 (2024) : 469-481 . |
| APA | Feng, Qianxue , Wang, Mingjie , Chen, Weiling , Zhao, Tiesong , Zhu, Yi . Pixel-Level Sonar Image JND Based on Inexact Supervised Learning . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI , 2024 , 14435 , 469-481 . |
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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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Abstract ：
由于网络环境的多变性，视频播放过程中容易出现卡顿、比特率波动等情况，严重影响了终端用户的体验质量. 为优化网络资源分配并提升用户观看体验，准确评估视频质量至关重要. 现有的视频质量评价方法主要针对短视频，普遍关注人眼视觉感知特性，较少考虑人类记忆特性对视觉信息的存储和表达能力，以及视觉感知和记忆特性之间的相互作用. 而用户观看长视频的时候，其质量评价需要动态评价，除了考虑感知要素外，还要引入记忆要素.为了更好地衡量长视频的质量评价，本文引入深度网络模型，深入探讨了视频感知和记忆特性对用户观看体验的影响，并基于两者特性提出长视频的动态质量评价模型. 首先，本文设计主观实验，探究在不同视频播放模式下，视觉感知特性和人类记忆特性对用户体验质量的影响，构建了基于用户感知和记忆的视频质量数据库（Video Quality Database with Perception and Memory，PAM-VQD）；其次，基于 PAM-VQD 数据库，采用深度学习的方法，结合视觉注意力机制，提取视频的深层感知特征，以精准评估感知对用户体验质量的影响；最后，将前端网络输出的感知质量分数、播放状态以及自卡顿间隔作为三个特征输入长短期记忆网络，以建立视觉感知和记忆特性之间的时间依赖关系. 实验结果表明，所提出的质量评估模型在不同视频播放模式下均能准确预测用户体验质量，且泛化性能良好.
Keyword ：
体验质量 体验质量 注意力机制 注意力机制 深度学习 深度学习 视觉感知特性 视觉感知特性 记忆效应 记忆效应
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| GB/T 7714 | 林丽群 , 暨书逸 , 何嘉晨 et al. 基于感知和记忆的视频动态质量评价 [J]. | 电子学报 , 2024 . |
| MLA | 林丽群 et al. "基于感知和记忆的视频动态质量评价" . | 电子学报 (2024) . |
| APA | 林丽群 , 暨书逸 , 何嘉晨 , 赵铁松 , 陈炜玲 , 郭宗明 . 基于感知和记忆的视频动态质量评价 . | 电子学报 , 2024 . |
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