Joint modeling and reconstruction of a compressively-sensed set of correlated images

被引:16
作者
Chang, Kan [1 ]
Li, Baoxin [2 ]
机构
[1] Guangxi Univ, Sch Comp & Elect Informat, Nanning 530004, Peoples R China
[2] Arizona State Univ, Dept Comp Sci & Engn, Tempe, AZ 85287 USA
关键词
Compressive sensing; Correlated images; Intra-image correlation; Inter-image correlation; Inter-channel correlation; Total variation; Non-local means; Group sparsity; QUALITY ASSESSMENT; INFORMATION; RECOVERY; REGULARIZATION; ALGORITHMS; SPARSITY;
D O I
10.1016/j.jvcir.2015.09.020
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Employing correlation among images for improved reconstruction in compressive sensing is a conceptually attractive idea, although developing efficient modeling strategies and reconstruction algorithms are often the key to achieve any potential benefit. This paper presents a novel modeling strategy and an efficient reconstruction algorithm for processing a set of correlated images, jointly taking into consideration inter-image correlation, intra-image correlation and inter-channel correlation. The approach starts with joint modeling of the entire image set in the gradient domain, which supports simultaneous representation of local smoothness, nonlocal self-similarity of every single image, and inter-image correlation. Then an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. Furthermore, to support color image reconstruction, the proposed algorithm is extended by using the concept of group sparsity to explore inter-channel correlation. The effectiveness of the proposed approach is demonstrated with extensive experiments on both grayscale and color image sets. Results are also compared with recently proposed compressive sensing recovery algorithms. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:286 / 300
页数:15
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