Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting

TL;DR

This paper introduces two blind inpainting methods using  minimization for restoring images corrupted by impulse and mixed Gaussian noise, demonstrating improved performance and convergence properties.

Contribution

The paper proposes novel blind inpainting algorithms based on  minimization that effectively identify damaged pixels and restore images, with proven convergence analysis.

Findings

Methods outperform existing techniques in experiments.

Algorithms converge to coordinatewise and local minimum points.

Proposed approach effectively handles impulse and mixed Gaussian noise.

Abstract

This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.