Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting
This addresses image restoration for applications like photography or medical imaging, but it is incremental as it builds on existing blind inpainting techniques.
The paper tackles the problem of restoring images corrupted by impulse noise and mixed Gaussian impulse noise by proposing two methods based on blind inpainting and ℓ₀ minimization that simultaneously identify damaged pixels and restore the image, achieving better performance than other methods in experiments.
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.