Abstract
In order to further improve the contrast and clarity of the fused image, a multi-focus image fusion algorithm based on convolutional sparse representation with mask simulation (CSRMS) is proposed. Firstly, auxiliary variable alternation with additive mask simulation is applied to convolutional dictionary filters learning. Then, we propose CSRMS-based multi-focus image fusion framework, in which each source image is decomposed into base layer and detail layer. Lastly, six classical multi-focus images are used to demonstrate that our method outperforms the CSR-based method in terms of both objective assessment and visual quality. Moreover, the brightness of our method is higher than other methods and avoids boundary artifacts.
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