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

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.

18 pages, 4 figures

Cited by in corpus (6)

• Total variation with overlapping group sparsity for image deblurring under impulse noise
• Analysis and optimisation of a variational model for mixed Gaussian and Salt & Pepper noise removal
• A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
• Linearly-involved Moreau-Enhanced-over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression
• Context-Aware Mutual Learning for Blind Image Inpainting and Beyond
• Robust regression for mixed Poisson-Gaussian model