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Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting
Optimization and Control · Submitted 4 Apr 2013
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. | background 0.91 | 0.47 | 0.04 | asserted 0.96 |
| s1 | 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. | background 0.84 | 0.06 | 0.03 | asserted 0.99 |
| s2 | We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. | contribution 0.80 | 0.95 | 0.32 | asserted 0.96 |
| s3 | 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. | result 0.96 | 0.94 | 0.53 | asserted 1.00 |
| s4 | In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points. | contribution 0.74 | 0.93 | 0.14 | asserted 1.00 |
| s5 | In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms. | result 0.77 | 0.86 | 0.09 | asserted 0.63 |
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The paper
Original 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.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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The Poisson component, which relates to the quantum nature of lights, models the impulsive noise, and the Gaussian component, which relates to the noise present in the electronic part of the imaging system [ref] .
Modelling Hospital length of stay using convolutive mixtures distributions · Link with image processing · 0.85
What it cites · 3 sentences without a confident label
Compares with 1 paper
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The other is the penalty decomposition method (PD) by Lu and Zhang [ref] for the problem [equation] $λ_1$ is tuned to achieve the best quality of the restoration images.
Sparse Approximation via Penalty Decomposition Methods · Numerical Experiments · 0.99
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