Adaptively Sparse Regularization for Blind Image Restoration

TL;DR

This paper introduces an adaptively sparse regularization approach for blind image restoration, combining high- and low-order gradients with an entropy-based adaptive operator, leading to improved blur kernel and image recovery accuracy.

Contribution

It proposes a novel hybrid regularization method with an adaptive operator for blind image restoration, enhancing convergence and recovery performance.

Findings

Outperforms existing blind deblurring methods in accuracy

Effective on various blur kernels and images

Demonstrates superior convergence properties

Abstract

Image quality is the basis of image communication and understanding tasks. Due to the blur and noise effects caused by imaging, transmission and other processes, the image quality is degraded. Blind image restoration is widely used to improve image quality, where the main goal is to faithfully estimate the blur kernel and the latent sharp image. In this study, based on experimental observation and research, an adaptively sparse regularized minimization method is originally proposed. The high-order gradients combine with low-order ones to form a hybrid regularization term, and an adaptive operator derived from the image entropy is introduced to maintain a good convergence. Extensive experiments were conducted on different blur kernels and images. Compared with existing state-of-the-art blind deblurring methods, our method demonstrates superiority on the recovery accuracy.