Removing out-of-focus blur from a single image

TL;DR

This paper introduces a blind deconvolution method for removing defocus blur from single images, addressing segmentation errors and improving kernel estimation for clearer all-in-focus images.

Contribution

It presents a novel blind deconvolution approach that suppresses segmentation artifacts and enhances defocus kernel estimation using non-parametric constraints.

Findings

Outperforms existing methods on real datasets

Effectively suppresses artifacts caused by segmentation errors

Achieves more accurate defocus kernel estimation

Abstract

Reproducing an all-in-focus image from an image with defocus regions is of practical value in many applications, eg, digital photography, and robotics. Using the output of some existing defocus map estimator, existing approaches first segment a de-focused image into multiple regions blurred by Gaussian kernels with different variance each, and then de-blur each region using the corresponding Gaussian kernel. In this paper, we proposed a blind deconvolution method specifically designed for removing defocus blurring from an image, by providing effective solutions to two critical problems: 1) suppressing the artifacts caused by segmentation error by introducing an additional variable regularized by weighted $\ell_0$-norm; and 2) more accurate defocus kernel estimation using non-parametric symmetry and low-rank based constraints on the kernel. The experiments on real datasets showed the advantages of the proposed method over existing ones, thanks to the effective treatments of the two important issues mentioned above during deconvolution.