Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification

Most existing sparse representation-based (SR) fusion methods consider the local information of each image patch independently during fusion. Some spatial artifacts are easily introduced to the fused image. A sliding window technology is often employed by these methods to overcome this issue. However, this comes at the cost of high computational complexity. Alternatively, we come up with a novel multi-focus image fusion method that takes full consideration of the strong correlations among spatially adjacent image patches with NO need for a sliding window. To this end, a non-negative SR model with local consistency constraint (CNNSR) on the representation coefficients is first constructed to encode each image patch. Then a patch-level consistency rectification strategy is presented to merge the input image patches, by which the spatial artifacts in the fused images are greatly reduced. As well, a compact non-negative dictionary is constructed for the CNNSR model. Experimental results demonstrate that the proposed fusion method outperforms some state-of-the art methods. Moreover, the proposed method is computationally efficient, thereby facilitating real-world applications