Sparsity-Promoting Adaptive Coding with Robust Empirical Mode Decomposition for Image Restoration

In this paper, a novel data-driven sparse coding framework is proposed to solve image restoration problem based on a robust empirical mode decomposition. This powerful analysis tool for multi-dimensional signals can adaptively decompose images into multiscale oscillating components according to intrinsic modes of data self. This treatment can obtain very effective sparse representation, and meanwhile generates a dictionary at multiple geometric scales and frequency bands. The distribution of sparse coefficients is reliably approximated by generalized Gaussian model. Moreover, a sparse approximation of blur kernel is also obtained as a strong prior. Finally, latent image and blur kernel can be jointly estimated via alternating optimization scheme. The extensive experiments show that our approach can effectively and efficiently recover the sharpness of local structures and suppress undesirable artifacts.