Medical image restoration method via multiple nonlocal prior constraints

Medical image restoration is a fundamental issue in the area of medical signal processing, which aims recove high quality medical image from its degradation observation. Recently, the methods with nonlocal self-similarity prior have led to a great improvement on many medical image restoration tasks. Nevertheless, the nonlocal technique is generally embedded with only one kind of constraint, such as sparsity or low-rank in the conventional model, which limits their abilities and show good performance on certain prior. To address this problem, in this paper, we present a novel medical image restoration method with multiple nonlocal-based prior regularizations. The surfacelet transformation is introduced to construct a cubic sparsity constraint to a group of nonlocal similar patches. Likewise, due to the self-similarity existed in the medical image, two extra kinds of nonlocal-based priors, nonlocal total variation and nonlocal weighted low-rank, are also exploited to constrain the local smoothness and nonlocal relationship jointly. In this way, each of the designed priors can well recover a group of patches with similar structure. And then, the designed priors are combined into a unified proposed optimization framework, which will obtain the advantages from all of them simultaneously. Finally, to solve the objective function in the proposed framework, we develop an iterative numerical scenario based on alternating direction multipliers method. The extensive experiments on test medical images demonstrate that our proposed model outperforms the comparison methods on both of visual quality and objective evaluation results. © 2020 - IOS Press and the authors. All rights reserved.