Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting
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Abstract (arXiv)
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 ℓ0 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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2013.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2023A Comparison of Image Denoising Methodscited by 7
- 2026Context-Aware Mutual Learning for Blind Image Inpainting and Beyondcited by 0
- 2026Context-Aware Mutual Learning for Blind Image Inpainting and Beyond13 Sep 2026
- 2023A Comparison of Image Denoising Methods18 Apr 2023