K. Chang and B. Li, “Joint modeling and reconstruction of a compressively-sensed set of correlated images”, Journal of Visual Communication and Image Representation, Vol. 33, pp. 286-300. November, 2015.


Joint Modeling and Reconstruction of A Compressively-sensed Set of Correlated Images

Kan Chang and Baoxin Li


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 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.


Source Code

To facilitate further evaluation and exploration of the method proposed in the above paper, we publish the source code at this link.

You are free to use the source code provided that (1) you clearly cite the source; and (2) you do not make any redistribution of the code.