An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution

TL;DR

This paper introduces an adaptive guided filter-based image deconvolution method that automatically estimates regularization parameters, simplifying the process and improving restoration quality compared to existing techniques.

Contribution

It proposes a novel automatic parameter estimation approach for guided filter-based deconvolution, reducing complexity and enhancing performance.

Findings

Outperforms state-of-the-art methods in ISNR

Achieves better visual quality in deblurred images

Simplifies parameter tuning process

Abstract

Image deconvolution is still to be a challenging ill-posed problem for recovering a clear image from a given blurry image, when the point spread function is known. Although competitive deconvolution methods are numerically impressive and approach theoretical limits, they are becoming more complex, making analysis, and implementation difficult. Furthermore, accurate estimation of the regularization parameter is not easy for successfully solving image deconvolution problems. In this paper, we develop an effective approach for image restoration based on one explicit image filter - guided filter. By applying the decouple of denoising and deblurring techniques to the deconvolution model, we reduce the optimization complexity and achieve a simple but effective algorithm to automatically compute the parameter in each iteration, which is based on Morozov's discrepancy principle. Experimental results demonstrate that the proposed algorithm outperforms many state-of-the-art deconvolution methods in terms of both ISNR and visual quality.