Multi-focus noisy image fusion based on gradient regularized convolutional sparse representatione

Abstract

The method proposes a multi-focus noisy image fusion algorithm combining gradient regularized convolutional sparse representatione and spatial frequency. Firstly, the source image is decomposed into a base layer and a detail layer through two-scale image decomposition. The detail layer uses the Alternating Direction Method of Multipliers (ADMM) to solve the convolutional sparse coefficients with gradient penalties to complete the fusion of detail layer coefficients. Then, The base layer uses the spatial frequency to judge the focus area, the spatial frequency and the "choose-max" strategy are applied to achieved the multi-focus fusion result of base layer. Finally, the fused image is calculated as a superposition of the base layer and the detail layer. Experimental results show that compared with other algorithms, this algorithm provides excellent subjective visual perception and objective evaluation metrics.