Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior

It is challenging to simultaneously achieve noise suppression and fine detail preservation in noisy image fusion. To address this challenge, we propose a novel strategy for noisy image fusion. Assuming that an image can be modeled as a superposition of low-rank and sparse (LR-and-S) components, we develop a novel discriminative dictionary learning algorithm to construct two dictionaries so as to decompose the input image into LR-and-S components. Specifically, to make dictionary possess discriminative power, we enforce spatial morphology constraint on each dictionary. Furthermore, we develop within-class consistency constraint by exploiting the similarity of low-rank components and impose it on the coding coefficients to further improve the discriminative power of the learned dictionary. In image decomposition, external patch prior and internal self-similarity prior of an image are exploited to build image decomposition model, based on which the latent subspace for fusion and recovery is estimated by minimizing rank-regularization of the subspace learned via clustering of similar patches. To construct different components of fused result, we use l1-norm maximization rule to fuse the decomposed components. Finally, the fused image is obtained by adding the fused components together. Experiments demonstrate that our method outperforms several state-of-the-art methods in terms of both objective quality assessment and subjective visual perception.