Optimizing the regularization parameters of prior information in sparse coding-based multispectral image fusion

Abstract

Advanced sparse coding-based image fusion methods use some prior information to fuse low-resolution multispectral (LR-MS) and panchromatic images to create a high-resolution multispectral image (HR-MS). This information mainly includes a sparsity term, spectral unmixing, and nonlocal similarities. These prior terms are usually considered in the sparse optimization problem as constraints with specific regularization parameters. During the optimization, the regularization parameter of each prior term is optimized by considering the other two prior terms as constants. This study aims to simultaneously optimize the regularization parameters of prior terms in a sparse coding image fusion method to construct an HR-MS from input LR-MS and Pan images. Several optimization methods, including particle swarm optimization, ant colony optimization, differential evolution, and genetic algorithm were used to optimize the regularization parameters. The results showed that particle swarm optimization had the highest performance in increasing the peak signal-to-noise ratio on the dataset available from the study area. The advantages of the proposed optimized sparse coding (OSC) approach are the ability to, 1) preserve spatial details while eliminating spectral distortions, 2) simultaneously optimize the regularization parameters of prior terms in a sparse coding image fusion framework, 3) considering nonlocal similarities to enhance fusion result, and 4) promising fusion results over heterogeneous regions with highly spectral variations. The relative dimensionless global error in synthesis, spectral angle mapper, universal image quality index, and peak signal to noise ratio criteria were at least 0.76, 1.16, 0.0257, and 2.68 better than those achieved by conventional PS methods, i.e., Gram-Schmidt, Brovey transform, generalized intensity-hue-saturation, smoothing filter-based intensity modulation, and a novel sparse coding-based image fusion method. According to the results, better preservation of spatial details and lower spectral distortions can be achieved using the proposed OSC approach.