Infrared and visible image fusion based on random projection and sparse representation

Abstract

A new image fusion approach for infrared and visible images is explored, combining fusion with data compression based on sparse representation and compressed sensing. The proposed approach first compresses the sensing data by random projection and then obtains sparse coefficients on compressed samples by sparse representation. Finally, the fusion coefficients are combined with the fusion impact factor and the fused image is reconstructed from the combined sparse coefficients. Experimental results validate its rationality and effectiveness, which can achieve comparable fusion quality on the less-compressed sensing data.