Remote-sensing Fusion by Multiscale Block-based Compressed Sensing
- DOI
- 10.2991/nceece-15.2016.280How to use a DOI?
- Keywords
- Compressed sensing; Remote sensing; Image fusion; Iterative thresholding projection.
- Abstract
A new compressive fusion algorithm based on non-uniform sampling is proposed. Although conventional block-based compressed sensing (BCS) represents a low computational cost, it suffers from low reconstruction quality since it is not well accounting for global image features. Employing the structured random matrix, multiscale non-uniform BCS (MNBCS) is implemented with decomposition level-dependent block-sizes and subrates. The proposed methodology improves the reconstruction quality without impacting the computational complexity. Experimental results show that the iterative soft-thresholding projection (ISTP) reconstruction with MNBCS achieves a higher reconstruction quality and a lower computational cost than. At very low sampling rates, MNBCS outperforms traditional wavelet-based fusion techniques.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Senlin Yang AU - Xin Chong PY - 2015/12 DA - 2015/12 TI - Remote-sensing Fusion by Multiscale Block-based Compressed Sensing BT - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering PB - Atlantis Press SP - 1557 EP - 1560 SN - 2352-5401 UR - https://doi.org/10.2991/nceece-15.2016.280 DO - 10.2991/nceece-15.2016.280 ID - Yang2015/12 ER -