Fast blind image deblurring via patch-wise maximum content-weighted prior

Blind image deblurring aims to derive the kernel and corresponding clear version solely from blurred images. This paper introduces an innovative blind image deblurring method based on the patch-wise maximum content-weighted prior (PMCW). Our work originates from the intuitive observation that the maximum content-weighted value of non-overlapping patches will significantly decrease after blurring degradation, which we demonstrate both mathematically and empirically. Building upon this observation, we propose a novel blind deblurring model combining $L_0$-regularized PMCW and $L_0$-regularized gradient prior, and develop an efficient solution algorithm utilizing projected alternating minimization (PAM). Qualitative and quantitative evaluation results on multiple benchmark datasets indicate that our proposed model achieves optimal performance, surpassing state-of-the-art algorithms in solving efficiency and various quantitative metrics.