Multimodal image fusion via sparse representation with local patch dictionaries

Sparse representation is a promising technique for the field of image processing and pattern recognition. It generally exploits over-complete dictionaries which is fixed and known in advance, or learned using training algorithm such as K-SVD. In this paper, we propose a new multimodal image fusion approach based on the sparsity model with local patch dictionaries generated directly from input images. For every location in the image, dictionary is simply constructed with neighboring patches. Experimental results show that the proposed method is efficient and competitive with some existing image fusion methods.