An image fusion framework using morphology and sparse representation

Image fusion is the process which aims to integrate the relevant and complementary information from a set of images into a single comprehensive image. Sparse representation (SR) is a powerful technique used in a wide variety of applications like denoising, compression and fusion. Building a compact and informative dictionary is the principal challenge in these applications. Hence, we propose a supervised classification based learning technique for the fusion algorithm. As an initial step, each patch of the training data set is pre-classified based on their gradient dominant direction. Then, a dictionary is learned using K-SVD algorithm. With this universal dictionary, sparse coefficients are estimated using greedy OMP algorithm to represent the given set of source images in the dominant direction. Finally, the Euclidean norm is used as a distance measure to reconstruct the fused image. Experimental results on different types of source images demonstrate the effectiveness of the proposed algorithm with conventional methods in terms of visual and quantitative evaluations.