IEICE TRANSACTIONS on Fundamentals
Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection
Summary :
In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.6 pp.1125-1132
- Publication Date
- 2007/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.6.1125
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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Xiaowei ZHANG, Nuo ZHANG, Jianming LU, Takashi YAHAGI, "Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 6, pp. 1125-1132, June 2007, doi: 10.1093/ietfec/e90-a.6.1125.
Abstract: In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.6.1125/_p
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@ARTICLE{e90-a_6_1125,
author={Xiaowei ZHANG, Nuo ZHANG, Jianming LU, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection},
year={2007},
volume={E90-A},
number={6},
pages={1125-1132},
abstract={In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.},
keywords={},
doi={10.1093/ietfec/e90-a.6.1125},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1125
EP - 1132
AU - Xiaowei ZHANG
AU - Nuo ZHANG
AU - Jianming LU
AU - Takashi YAHAGI
PY - 2007
DO - 10.1093/ietfec/e90-a.6.1125
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2007
AB - In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.
ER -
English
