MdedFusion: A multi-level detail enhancement decomposition method for infrared and visible image fusion

Abstract

To improve the infrared target features and preserve the texture details from source images, a simple and efficient multi-level detail enhancement decomposition method based on weighted least squares (WLS) and local statistical edge model (LSEM) is proposed, termed as MdedFusion. First, visibility enhancement of visible image is performed by using guided filter to avoid the effects of low-light environments. Then, infrared image and enhanced visible image are decomposed into a series of smooth parts and detail parts by WLS filter based multi-level decomposition mechanism. Next, in order to highlight edge features, a novel fusion strategy based on LSEM is proposed to make full use of variance difference for fusion of detail parts, and smooth parts are simply fused by weighted average (WAVG) strategy which is an efficient operation in our fusion framework. Finally, the fused image is obtained by reconstructing the fused smooth part and detail part. Specially, particle swarm optimization (PSO) algorithm is introduced to adaptively optimize the filter parameters in our framework. Compared with 16 state-of-the-art fusion methods, the experimental results show that the superiority of our proposed MdedFusion surpass the compared methods in highlighting infrared features and preserving texture detail information.