IMAGE FUSION VIA SLICE_BASED CONVOLUTIONAL SPARSE REPRESENTATION

For pixel-level image fusion, convolutional sparse representation model usually relies on the ADMM in the Fourier domain, generating many iterations and losing the sense of locality, which may result in fused images with blurred texture parts. To extract texture information of images more effectively, a slice-based convolutional sparse representation (SCSR) model is proposed, which is solved by an inertial approximation gradient method with dry friction (IPGM-DF) algorithm in the signal domain. IPGM-DF updates the dictionary and sparse coefficients simultaneously. Experimental results of image fusion show that the proposed method is superior to the convolutional sparse representation-based fusion method in both subjective and objective evaluation, which are comparable to that of SOTA.