Robust Edge Detection by Independent Component Analysis in Noisy Images

Xian-Hua HAN; Yen-Wei CHEN; Zensho NAKAO

Robust Edge Detection by Independent Component Analysis in Noisy Images

Xian-Hua HAN, Yen-Wei CHEN, Zensho NAKAO

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We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.9 pp.2204-2211

Publication Date: 2004/09/01

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Type of Manuscript: PAPER

Category: Image Processing and Video Processing

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Xian-Hua HAN, Yen-Wei CHEN, Zensho NAKAO, "Robust Edge Detection by Independent Component Analysis in Noisy Images" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 9, pp. 2204-2211, September 2004, doi: .
Abstract: We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_9_2204/_p

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@ARTICLE{e87-d_9_2204,
author={Xian-Hua HAN, Yen-Wei CHEN, Zensho NAKAO, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Edge Detection by Independent Component Analysis in Noisy Images},
year={2004},
volume={E87-D},
number={9},
pages={2204-2211},
abstract={We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.},
keywords={},
doi={},
ISSN={},
month={September},}

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TY - JOUR
TI - Robust Edge Detection by Independent Component Analysis in Noisy Images
T2 - IEICE TRANSACTIONS on Information
SP - 2204
EP - 2211
AU - Xian-Hua HAN
AU - Yen-Wei CHEN
AU - Zensho NAKAO
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2004
AB - We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.
ER -