Image restoration via exponential scale mixture‐based simultaneous sparse prior

Abstract

Image prior plays a decisive role in the performance of widely studied model-based restoration methods. To further improve restoration performance, this paper proposes an exponential scale mixture-based simultaneous sparse prior (ESM-SSP) to accurately characterize image prior information. Specifically, first, two structured dictionaries are adaptively learned to explore the local and non-local sparsity of similar patch groups simultaneously. Then, the exponential scale mixture (ESM) is employed to model simultaneous sparse coefficients. The adoption of ESM enables us to accurately estimate simultaneous sparse coefficients by adaptively adjusting the regularization parameters. With the aid of ESM-SSP, an effective image restoration algorithm is developed to preserve more image details. Extensive experimental results on image denoising and deblocking demonstrate that compared with many state-of-the-art model-based methods, the proposed ESM-SSP-based restoration algorithm not only has competitive peak signal-to-noise ratio, but also produces higher structural similarity index and better visuals. More importantly, the proposed method can also compete favourably with the superior deep learning-based restoration methods.