Enhancing Denoised Image Via Fusion with a Noisy Image

Jun Sang Yoo; Jong Ok Kim

doi:10.1109/ICIP.2019.8803164

Enhancing Denoised Image Via Fusion with a Noisy Image

Jun Sang Yoo, Jong Ok Kim

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.

Original language	English
Title of host publication	2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
Publisher	IEEE Computer Society
Pages	1790-1794
Number of pages	5
ISBN (Electronic)	9781538662496
DOIs	https://doi.org/10.1109/ICIP.2019.8803164
Publication status	Published - 2019 Sep
Event	26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China Duration: 2019 Sep 22 → 2019 Sep 25

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2019-September
ISSN (Print)	1522-4880

Conference

Conference	26th IEEE International Conference on Image Processing, ICIP 2019
Country/Territory	Taiwan, Province of China
City	Taipei
Period	19/9/22 → 19/9/25

Keywords

Image denoising
PCA
cost optimization
sparsity
texture

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2019.8803164

Cite this

Enhancing Denoised Image Via Fusion with a Noisy Image. / Yoo, Jun Sang; Kim, Jong Ok.

2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. IEEE Computer Society, 2019. p. 1790-1794 8803164 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoo, JS & Kim, JO 2019, Enhancing Denoised Image Via Fusion with a Noisy Image. in 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings., 8803164, Proceedings - International Conference on Image Processing, ICIP, vol. 2019-September, IEEE Computer Society, pp. 1790-1794, 26th IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, Province of China, 19/9/22. https://doi.org/10.1109/ICIP.2019.8803164

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abstract = "Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.",

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note = "Funding Information: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2018-0-01421) supervised by the IITP (Institute of Information & communications Technology Planning & Evaluation) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1005834) Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",

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N2 - Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.

AB - Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.

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Enhancing Denoised Image Via Fusion with a Noisy Image

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