Remote sensing Fusion by Multiscale Block based Compressed Sensing
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.