Image restoration via Bayesian structured sparse coding

Abstract

In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) - if treated as a latent variable - can be jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR.