For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator.

Abstracts: This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning-based SIRR methods have shown significant performance degradation due to unnoticeable distortions and …

2022年10月10日 · In this paper, we propose to address the stereo image restoration with transformer by leveraging its powerful capability of modeling long-range context dependencies. Specifically, we construct a stereo image restoration transformer (SIR-Former) to effectively exploit the cross-view correlations.

These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

IEEE / CVF 计算机视觉和模式识别会议 (The IEEE / CVF Computer Vision and Pattern Recognition Conference，CVPR) 是首屈一指的一年一度计算机视觉盛会，包括主要会议和多个同地研讨会和短期课程，被认为是该领域最重要的会议之一。

2023年8月22日 · Extensive experiments on Nature, SIR 2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes. Published in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image . 通过从图像中分离噪声的自监督图像去噪的 循环多变量函数 CVPR'2022. 作者来自韩国首尔大学，该团队在CVPR'2022 还发表了另一篇自监督去噪文章 AP-BSN，对于该文章的 …

Official implementation of the paper "CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image" (CVPR 2022)

2023年2月24日 · 提出了一种新的循环多变量函数（cvf），它将其输入分解为多个子分量，并将其输出的组合重新作为输入。利用cvf设计了用于srgb图像去噪的自监督图像去噪模型（cvf-sid）。

CVPR，全称为“Conference on Computer Vision and Pattern Recognition”，即计算机视觉与模式识别会议，是计算机视觉领域中最重要的国际会议之一。 该会议汇集了来自世界各地的研究人员、学者和工程师，旨在分享最新的计算机视觉和模式识别研究成果，讨论行业趋势，以及促进领域内的合作与交流。 CVPR2022官网： Home | CVPR 2022 (thecvf.com) 社交账号：