Pub Date : 2025-12-17DOI: 10.1109/tip.2025.3642557
Xinyue Li, Zhangkai Ni, Hang Wu, Wenhan Yang, Hanli Wang, Lianghua He, Sam Kwong
{"title":"Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization","authors":"Xinyue Li, Zhangkai Ni, Hang Wu, Wenhan Yang, Hanli Wang, Lianghua He, Sam Kwong","doi":"10.1109/tip.2025.3642557","DOIUrl":"https://doi.org/10.1109/tip.2025.3642557","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"252 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/tip.2025.3642633
Xi Yang, Wenjiao Dong, Xian Wang, De Cheng, Nannan Wang
{"title":"FA-Net: A Feature Alignment Network for Video-based Visible-Infrared Person Re-Identification","authors":"Xi Yang, Wenjiao Dong, Xian Wang, De Cheng, Nannan Wang","doi":"10.1109/tip.2025.3642633","DOIUrl":"https://doi.org/10.1109/tip.2025.3642633","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"155 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/tip.2025.3640863
Lingchen Sun, Rongyuan Wu, Jie Liang, Zhengqiang Zhang, Hongwei Yong, Lei Zhang
{"title":"Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution","authors":"Lingchen Sun, Rongyuan Wu, Jie Liang, Zhengqiang Zhang, Hongwei Yong, Lei Zhang","doi":"10.1109/tip.2025.3640863","DOIUrl":"https://doi.org/10.1109/tip.2025.3640863","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"1 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1109/tip.2025.3638153
Kui Jiang, Junjun Jiang, Xianming Liu, Hongxun Yao, Chia-Wen Lin
{"title":"PH-Mamba: Enhancing Mamba with Position Encoding and Harmonized Attention for Image Deraining and Beyond","authors":"Kui Jiang, Junjun Jiang, Xianming Liu, Hongxun Yao, Chia-Wen Lin","doi":"10.1109/tip.2025.3638153","DOIUrl":"https://doi.org/10.1109/tip.2025.3638153","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"66 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1109/tip.2025.3641052
Jiachuan Yu,Han Sun,Yuankai Zhou,Xiaowei Jiang
This paper presents a robust, decoupled approach to camera distortion correction using a rational function model (RFM), designed to address challenges in accuracy and flexibility within precision-critical applications. Camera distortion is a pervasive issue in fields such as medical imaging, robotics, and 3D reconstruction, where high fidelity and geometric accuracy are crucial. Traditional distortion correction methods rely on radial-symmetry-based models, which have limited precision under tangential distortion and require nonlinear optimization. In contrast, general models do not rely on radial symmetry geometry and are theoretically generalizable to various sources of distortion. There exists a gap between the theoretical precision advantage of the Rational Function Model (RFM) and its practical applicability in real-world scenarios. This gap arises from uncertainties regarding the model's robustness to noise, the impact of sparse sample distributions, and its generalizability out of the training sample range. In this paper, we provide a mathematical interpretation of how RFM is suitable for the distortion correction problem through sensitivity analysis. The precision and robustness of RFM are evaluated through synthetic and real-world experiments, considering distortion level, noise level, and sample distribution. Moreover, a practical and accurate decoupled distortion correction method is proposed using just a single captured image of a chessboard pattern. The correction performance is compared with the current state-of-the-art using camera calibration, and experimental results indicate that more precise distortion correction can enhance the overall accuracy of camera calibration. In summary, this decoupled RFM-based distortion correction approach provides a flexible, high-precision solution for applications requiring minimal calibration steps and reliable geometric accuracy, establishing a foundation for distortion-free imaging and simplified camera models in precision-driven computer vision tasks.
本文提出了一种使用理性函数模型（RFM）的鲁棒解耦相机畸变校正方法，旨在解决精度关键应用中精度和灵活性方面的挑战。在医学成像、机器人和3D重建等领域，相机失真是一个普遍存在的问题，在这些领域，高保真度和几何精度至关重要。传统的畸变校正方法依赖于基于径向对称的模型，在切向畸变下精度有限，且需要非线性优化。相比之下，一般模型不依赖于径向对称几何，理论上可以推广到各种失真源。在Rational Function Model （RFM）的理论精度优势和它在现实场景中的实际适用性之间存在着差距。这种差距源于模型对噪声的鲁棒性、稀疏样本分布的影响以及其在训练样本范围外的泛化性的不确定性。在本文中，我们通过灵敏度分析提供了RFM如何适用于失真校正问题的数学解释。通过综合考虑失真水平、噪声水平和样本分布，对RFM的精度和鲁棒性进行了评价。此外，本文还提出了一种实用且精确的解耦畸变校正方法。实验结果表明，更精确的畸变校正可以提高摄像机标定的整体精度。总之，这种解耦的基于rfm的畸变校正方法为需要最小校准步骤和可靠几何精度的应用提供了灵活、高精度的解决方案，为精确驱动的计算机视觉任务中的无畸变成像和简化相机模型奠定了基础。
{"title":"High-Precision Camera Distortion Correction: A Decoupled Approach with Rational Functions.","authors":"Jiachuan Yu,Han Sun,Yuankai Zhou,Xiaowei Jiang","doi":"10.1109/tip.2025.3641052","DOIUrl":"https://doi.org/10.1109/tip.2025.3641052","url":null,"abstract":"This paper presents a robust, decoupled approach to camera distortion correction using a rational function model (RFM), designed to address challenges in accuracy and flexibility within precision-critical applications. Camera distortion is a pervasive issue in fields such as medical imaging, robotics, and 3D reconstruction, where high fidelity and geometric accuracy are crucial. Traditional distortion correction methods rely on radial-symmetry-based models, which have limited precision under tangential distortion and require nonlinear optimization. In contrast, general models do not rely on radial symmetry geometry and are theoretically generalizable to various sources of distortion. There exists a gap between the theoretical precision advantage of the Rational Function Model (RFM) and its practical applicability in real-world scenarios. This gap arises from uncertainties regarding the model's robustness to noise, the impact of sparse sample distributions, and its generalizability out of the training sample range. In this paper, we provide a mathematical interpretation of how RFM is suitable for the distortion correction problem through sensitivity analysis. The precision and robustness of RFM are evaluated through synthetic and real-world experiments, considering distortion level, noise level, and sample distribution. Moreover, a practical and accurate decoupled distortion correction method is proposed using just a single captured image of a chessboard pattern. The correction performance is compared with the current state-of-the-art using camera calibration, and experimental results indicate that more precise distortion correction can enhance the overall accuracy of camera calibration. In summary, this decoupled RFM-based distortion correction approach provides a flexible, high-precision solution for applications requiring minimal calibration steps and reliable geometric accuracy, establishing a foundation for distortion-free imaging and simplified camera models in precision-driven computer vision tasks.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"38 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1109/tip.2025.3638126
Junteng Zhang, Tong Chen, Dandan Ding, Zhan Ma
{"title":"Neural Compression System for Point Cloud Video Streaming","authors":"Junteng Zhang, Tong Chen, Dandan Ding, Zhan Ma","doi":"10.1109/tip.2025.3638126","DOIUrl":"https://doi.org/10.1109/tip.2025.3638126","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"3 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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