Multi-Source Image Fusion Based on Low-Rank Decomposition and Convolutional Sparse Coding

Aiming at the feature that convolutional sparse coding can better retain image information features, a multi-source image fusion method based on low-rank decomposition and convolutional sparse coding is proposed. In order to avoid the impact of image block processing on the image structure, each image to be fused is processed globally. First, the image is decomposed into low-rank and sparse parts by low-rank decomposition. Then, a set of sparse filter dictionaries can be trained by convolution decomposition of sparse parts, and the convolution sparse coding is applied to image fusion. Second, different fusion rules are designed for the low-rank and sparse components to obtain the low-rank and sparse components, and finally the fusion image is obtained. Finally, in order to verify the fusion effect of the proposed method, the proposed method is compared with other methods. The experimental results show that the proposed method has achieved good results in terms of visual effects and objective evaluation indicators.