Parallel Multispectral Image Super-resolution Based on Sparse Representations

Image super-resolution (SR) produces a high-resolution (HR) image using either a single or multiple low-resolution (LR) input image(s) of the same scene. Sparse representation techniques are effectively applied for image SR because of their high reconstruction accuracy. SR reconstruction is accomplished through learning of an overcomplete dictionary and then solving a series of regularization problems applied on each patch extracted from the input image. This paper demonstrates a sparse representation based coupled overcomplete dictionary training and SR procedure for LR multispectral images. The proposed work is also implemented using a multicore parallel processing technique to provide faster reconstruction. Experimental results show superiority of the proposed method over some others.