Multi-morphology image super-resolution via sparse representation

Abstract

A novel multi-morphology image super-resolution algorithm via sparse representation is proposed in this paper. Firstly, the observed low resolution image is decomposed into a linear combination of morphological components by morphological component analysis. Secondly, for effective representation of different components, each dictionary pair is learned from the low resolution component and corresponding high resolution component training sets. Then, each high resolution morphological component is generated by combine the sparse coefficients of low resolution morphological component with the corresponding high resolution component dictionary. Finally, fusing all high resolution morphological components yields the final high resolution image. Experimental results on various natural and intelligent transport systems surveillance images demonstrate the superiority of the proposed method in terms of qualitative and quantitative performance.