On the simplification of multi-focus image fusion using dictionary-based sparse representation

This paper proposes a fast implementation for multi-focus image fusion using dictionary-based sparse representation. The proposed method reduces the computation complexity of the method in [1] by synthesizing feature signals from the trained sparse coefficient feature vectors for classifying each pixel in a source image as focused or defocused, which help remove the computation of the OMP algorithm [3] in [1]. As a result, the complexity of the proposed method can be only 1/100 of [1]. Simulation results further demonstrate that the fused image of the proposed method has the same quality as that of [1].