{"title":"通过基于非局部低阶残差的 ADMM 框架进行图像压缩传感重建","authors":"Junhao Zhang , Kim-Hui Yap , Lap-Pui Chau , Ce Zhu","doi":"10.1016/j.cviu.2024.104204","DOIUrl":null,"url":null,"abstract":"<div><div>The nonlocal low-rank (LR) modeling has proven to be an effective approach in image compressive sensing (CS) reconstruction, which starts by clustering similar patches using the nonlocal self-similarity (NSS) prior into nonlocal image group and then imposes an LR penalty on each nonlocal image group. However, most existing methods only approximate the LR matrix directly from the degraded nonlocal image group, which may lead to suboptimal LR matrix approximation and thus obtain unsatisfactory reconstruction results. In this paper, we propose a novel nonlocal low-rank residual (NLRR) approach for image CS reconstruction, which progressively approximates the underlying LR matrix by minimizing the LR residual. To do this, we first use the NSS prior to obtaining a good estimate of the original nonlocal image group, and then the LR residual between the degraded nonlocal image group and the estimated nonlocal image group is minimized to derive a more accurate LR matrix. To ensure the optimization is both feasible and reliable, we employ an alternative direction multiplier method (ADMM) to solve the NLRR-based image CS reconstruction problem. Our experimental results show that the proposed NLRR algorithm achieves superior performance against many popular or state-of-the-art image CS reconstruction methods, both in objective metrics and subjective perceptual quality.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image compressive sensing reconstruction via nonlocal low-rank residual-based ADMM framework\",\"authors\":\"Junhao Zhang , Kim-Hui Yap , Lap-Pui Chau , Ce Zhu\",\"doi\":\"10.1016/j.cviu.2024.104204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The nonlocal low-rank (LR) modeling has proven to be an effective approach in image compressive sensing (CS) reconstruction, which starts by clustering similar patches using the nonlocal self-similarity (NSS) prior into nonlocal image group and then imposes an LR penalty on each nonlocal image group. However, most existing methods only approximate the LR matrix directly from the degraded nonlocal image group, which may lead to suboptimal LR matrix approximation and thus obtain unsatisfactory reconstruction results. In this paper, we propose a novel nonlocal low-rank residual (NLRR) approach for image CS reconstruction, which progressively approximates the underlying LR matrix by minimizing the LR residual. To do this, we first use the NSS prior to obtaining a good estimate of the original nonlocal image group, and then the LR residual between the degraded nonlocal image group and the estimated nonlocal image group is minimized to derive a more accurate LR matrix. To ensure the optimization is both feasible and reliable, we employ an alternative direction multiplier method (ADMM) to solve the NLRR-based image CS reconstruction problem. Our experimental results show that the proposed NLRR algorithm achieves superior performance against many popular or state-of-the-art image CS reconstruction methods, both in objective metrics and subjective perceptual quality.</div></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision and Image Understanding\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077314224002856\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002856","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
非局部低阶(LR)建模已被证明是图像压缩传感(CS)重建中的一种有效方法,它首先利用非局部自相似性(NSS)先验将相似斑块聚类为非局部图像组,然后对每个非局部图像组施加 LR 惩罚。然而,大多数现有方法只是直接从退化的非局部图像组近似 LR 矩阵,这可能会导致 LR 矩阵近似效果不理想,从而得到不尽人意的重建结果。在本文中,我们提出了一种用于图像 CS 重建的新型非局部低阶残差(NLRR)方法,该方法通过最小化 LR 残差逐步逼近底层 LR 矩阵。为此,我们首先使用 NSS 先验法获得原始非本地图像组的良好估计值,然后最小化退化的非本地图像组和估计的非本地图像组之间的 LR 残差,从而得出更精确的 LR 矩阵。为确保优化的可行性和可靠性,我们采用了另一种方向乘法(ADMM)来解决基于 NLRR 的图像 CS 重建问题。我们的实验结果表明,与许多流行的或最先进的图像 CS 重建方法相比,所提出的 NLRR 算法在客观指标和主观感知质量方面都取得了优异的性能。
Image compressive sensing reconstruction via nonlocal low-rank residual-based ADMM framework
The nonlocal low-rank (LR) modeling has proven to be an effective approach in image compressive sensing (CS) reconstruction, which starts by clustering similar patches using the nonlocal self-similarity (NSS) prior into nonlocal image group and then imposes an LR penalty on each nonlocal image group. However, most existing methods only approximate the LR matrix directly from the degraded nonlocal image group, which may lead to suboptimal LR matrix approximation and thus obtain unsatisfactory reconstruction results. In this paper, we propose a novel nonlocal low-rank residual (NLRR) approach for image CS reconstruction, which progressively approximates the underlying LR matrix by minimizing the LR residual. To do this, we first use the NSS prior to obtaining a good estimate of the original nonlocal image group, and then the LR residual between the degraded nonlocal image group and the estimated nonlocal image group is minimized to derive a more accurate LR matrix. To ensure the optimization is both feasible and reliable, we employ an alternative direction multiplier method (ADMM) to solve the NLRR-based image CS reconstruction problem. Our experimental results show that the proposed NLRR algorithm achieves superior performance against many popular or state-of-the-art image CS reconstruction methods, both in objective metrics and subjective perceptual quality.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems