Exposure fusion based on sparse representation using approximate K-SVD

In this paper, we propose a novel exposure fusion scheme using the sparse representation theory, which can explore the sparseness of the source images. First, we present a novel way to get the chrominance information of the scene, and the saturation of the fused image can be adjusted using one user-controlled parameter. Second, we conduct the sparse representation on overlapping patches of luminance images obtained by ‘sliding window technique’, which use dictionary obtained by K-SVD with typical indoor and outdoor multiple exposure sequences. In addition, we introduce an efficient implementation of K-SVD (called approximate K-SVD) which can reduce complexity as well as memory requirements. Third, the coefficients are combined with a novel “frequency of atoms usage” fusion rule strategy. Finally, the fused image is reconstructed from the combined sparse coefficients and the used dictionary. Experiments show that the proposed method can give comparative results compared to state-of-art exposure fusion methods.