Infrared and visible image fusion based on two-scale decomposition and improved saliency detection

Abstract

Aiming at the problems of poor target saliency, loss of background information and time-consuming in image fusion, a fast image fusion algorithm combining two-scale decomposition and improved saliency detection is proposed. Mean filtering is used to decompose the source image into a base layer and a detail layer. The maximum symmetric surround (MSS) saliency detection algorithm is improved to obtain the dim suppressed MSS algorithm. dim suppressed MSS saliency detection and guided filtering is used to generate fusion rules for each layer. The inverse transformation of two-scale decomposition is used for the fusion sub-image of the base layer and the detail layer to obtain the final fusion result. Experimental results show that the algorithm consumes less time and has better fusion quality, which reflects the feasibility of the proposed algorithm.