This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images.First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images.Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to bovi-shield gold fp 5 l5 implement the weighted fusion of base-layer images adaptively.Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images.By this method, the gradient information concealed in toyo proxes st iii 305/40r22 the source images can be extracted as much as possible, then the fusion image owns more abundant edge information.
Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result.