Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation

Recently, various sparse representation methods have been successfully used in multi-focus image fusion. Most of them produce some spatial artifacts and blurring effects because they only consider the image local information due to the patch processing strategy. In order to reduce the spatial artifacts and blurring effects on the edge details and improve the robustness of the multi-focus image fusion, a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation is presented. The JCAS model, which integrates the analysis sparse representation and the synthesis sparse representation by using convolutional operation, can effectively separate large-scale structures and fine-scale textures of a single image. First, each source image is decomposed into a base layer and a detail layer using the JCAS model. Second, a Laplacian pyramid transform method is used to fuse the base layers, and a weighted regional energy method is used to fuse the detail layers. Finally, the fused image is reconstructed by combining the fused base and detail layers. Experimental results demonstrate that the proposed method can obtain clearer edge details compared with some popular multi-focus image fusion methods, thus exhibiting state-of-the-art performance in terms of both visual quality and objective assessment.

Automated summary

This study introduces a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation, which can effectively reduce spatial artifacts and blurring effects in multi-focus image fusion, producing clearer edge details.