A bilevel learning approach for nonlocal image deblurring with variable weights parameter

Abstract

This paper introduces an innovative bilevel optimization approach to elevate the deblurring process. By integrating a weights variable nonlocal model with a spatially varying attached term, the methodology aims to achieve enhanced restoration outcomes. Theoretical scrutiny is dedicated to unraveling the solution of the model, paving the way for the development of an efficient algorithm meticulously crafted to compute the clean image. This algorithm excels in learning both the weights parameter and the balanced L2-L1 attached parameter concurrently, thereby ensuring optimal performance. Through careful parameter selection, the proposed nonlocal deblurring model showcases superior effectiveness, surpassing existing models in terms of both performance and efficacy.