NDLI: A fine-grain nonlocal weighted average method for image CS reconstruction

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A fine-grain nonlocal weighted average method for image CS reconstruction

Content Provider	ACM Digital Library
Author	Tan, Yun Hou, Xingsong Qian, Xueming
Abstract	Compressive sensing can acquire a signal at a sampling rate far below the Nyquist sampling rate if signal is sparse in some domain. However, signal reconstruction from its observations is challenging because it is an implicit ill-pose problem in practice. As classical CS reconstruction methods, total variation (TV) and iteratively reweighted TV (ReTV) methods only exploit local image information, which results in some loss of image structures and causes blocking effect. In this paper, we observe there are abundant nonlocal repetitive structures in nature image, so we propose a novel fine-grain nonlocal weighted average method for nature image CS reconstruction, and we take full use of the nonlocal repetitive structures to recover image from the observations. Besides, an efficient iterative bound optimization algorithm which is stably convergent in our experiments is applied to the above CS reconstruction. The experimental results of different nature images demonstrate that our proposed algorithm can outperform the existing classical nature image CS reconstruction algorithms in Peak-Signal-Noise-Ratio (PSNR) and subjective evaluation.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450335287
DOI	10.1145/2808492.2808562
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cs reconstruction Compressive sensing Nonlocal repetitive Iterative algorithm
Content Type	Text
Resource Type	Article

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