Joint modeling and reconstruction of a compressively-sensed set of correlated images |
| |
| Affiliation: | 1. School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi 530004, China;2. Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA;1. Graduate Institute of Mathematics and Science Education, National Chiayi University, Chiayi 621, Taiwan, ROC;2. Department of Computer Science and Information Engineering, National Chiayi University, Chiayi 600, Taiwan, ROC;1. School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;2. School of Information and Engineering, Huzhou Teachers College, Huzhou 313000, China;1. Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan, ROC;2. Department of Information Technology, Tra Vinh University, Tra Vinh Province, Viet Nam;3. College of Information Technology, Da Nang University, Da Nang City, Viet Nam;1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia |
| |
| Abstract: | 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. |
| |
| Keywords: | Compressive sensing Correlated images Intra-image correlation Inter-image correlation Inter-channel correlation Total variation Non-local means Group sparsity |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
