TY - JOUR
T1 - Joint modeling and reconstruction of a compressively-sensed set of correlated images
AU - Chang, Kan
AU - Li, Baoxin
N1 - Funding Information:
The authors would like to thank anonymous reviewers for giving valuable suggestions to improve the quality of this manuscript. This work was partially supported by the Natural Science Foundation of China via Grants 61401108 and 61261023 , the Natural Science Foundation of Guangxi Zhuang Autonomous Region via Grant 2013GXNSFBA019272 . The support provided by China Scholarship Council ( No. 1402170001 ) during a visit of K. Chang to Arizona State University is also acknowledged. B. Li was partially supported by the Natural Science Foundation via Grants 1135616 and 0845469 .
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/11
Y1 - 2015/11
N2 - 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.
AB - 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.
KW - Compressive sensing
KW - Correlated images
KW - Group sparsity
KW - Inter-channel correlation
KW - Inter-image correlation
KW - Intra-image correlation
KW - Non-local means
KW - Total variation
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U2 - 10.1016/j.jvcir.2015.09.020
DO - 10.1016/j.jvcir.2015.09.020
M3 - Article
AN - SCOPUS:84944748321
SN - 1047-3203
VL - 33
SP - 286
EP - 300
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -