Infrared and Visible Image Fusion Based on Compressive Sensing and OSS-ICA-Bases

Abstract

Aimed at the problems that most existing fusion methods tolerate one or more drawback such as noise, blur and key information loss, a novel and valid fusion algorithm is proposed to efficiently extract the object information in infrared image and preserve abundant background information in visible image. Firstly, non-subsampled shearlet transform (NSST) is employed to decompose the visible and infrared images into high frequency subbands and low frequency subbands. Secondly, a fusion rule based on compressed sensing (CS) was put into high frequency subbands and a fusion rule based on online same scene independent component analysis bases (OSS-ICA-bases) was input into low frequency subbands. Finally the fusion image was reconstructed by an inverse NSST on these merged coefficients. Because the OSS-ICA-bases could suppress the noise and fuses the complementary information well, CS enables the high frequency subbands to be accurately reconstructed from fewer sparse fused coefficients, NSST can obtain the asymptotic optimal representation and has the better sparse representation ability, the proposed algorithm can obtain a better result. Experiments also show that our approach can achieve better performance than other methods in terms of subjective visual effect and objective assessment.