Multi-focus image fusion based on multi-scale sparse representation

Although colorful information in natural scenes can be collected, due to the limitation of camera depth of field, it is hard to capture an image with all-in-focus. Sparse representation (SR)-based methods have shown their powerful potentiality and ability in multi-focus image fusion. However, because of sparse coding and information compress, the existing fusion methods based on SR are imperfect to seize the rich details and significant texture information in source images. As a result, a fusion method based on multi-scale sparse representation for registered multi-focus images (MIF-MsSR) is proposed in this paper, where an adaptive fusion rule for sparse coefficients is presented. At first, source images are processed by multi-scale decomposition and sub-images with different scales can be obtained. According to image features with different richness in these sub-images, dictionaries with different sizes and redundancy are thereby trained. By comprehensively considering the relationships of focused areas, out-of-focused areas and boundary areas between the source images, an adaptive fusion rule based on l(0) - max and Sum Modified Laplacian (SML) is proposed. Finally, a fused image with all-in-focus can be obtained by sparse reconstruction and inverse multi-scale decomposition. Excessive experiments on multi-focus images have demonstrated that the proposed MIF-MsSR not only reserves the integrity of the information in source images, but also has better fusion performance on subjective and objective indicators than other state-of-the-art methods.

Automated summary

A fusion method based on multi-scale sparse representation for registered multi-focus images (MIF-MsSR) is proposed in this paper, with an adaptive fusion rule for sparse coefficients presented. Experiments have shown that the proposed method not only preserves the integrity of information in source images, but also outperforms other state-of-the-art methods in fusion performance on subjective and objective indicators.