An image enhancement technique based on wavelets

Hae Sung Lee; Yongbum Cho; Hyeran Byun; Jisang Yoo

doi:10.1007/3-540-45482-9_28

An image enhancement technique based on wavelets

Hae Sung Lee, Yongbum Cho, Hyeran Byun, Jisang Yoo

College of Computing

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

3 Citations (Scopus)

Abstract

We propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, a frame wavelet system designed as an optimal edge detector is used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm is tested on three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm is better than those of other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system. The experimental results also show that our algorithm has approximately same capability of deblocking as those of previous developed techniques.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Tomaso Poggio, Seong-Whan Lee, Heinrich H. Bulthoff
Publisher	Springer Verlag
Pages	286-296
Number of pages	11
ISBN (Print)	3540675604, 9783540675600
DOIs	https://doi.org/10.1007/3-540-45482-9_28
Publication status	Published - 2000
Event	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000 - Seoul, Korea, Republic of Duration: 2000 May 15 → 2000 May 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1811
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000
Country/Territory	Korea, Republic of
City	Seoul
Period	00/5/15 → 00/5/17

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45482-9_28

Cite this

Lee, H. S., Cho, Y., Byun, H., & Yoo, J. (2000). An image enhancement technique based on wavelets. In T. Poggio, S-W. Lee, & H. H. Bulthoff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 286-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_28

Lee, Hae Sung ; Cho, Yongbum ; Byun, Hyeran et al. / An image enhancement technique based on wavelets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Tomaso Poggio ; Seong-Whan Lee ; Heinrich H. Bulthoff. Springer Verlag, 2000. pp. 286-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, a frame wavelet system designed as an optimal edge detector is used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm is tested on three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm is better than those of other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system. The experimental results also show that our algorithm has approximately same capability of deblocking as those of previous developed techniques.",

author = "Lee, {Hae Sung} and Yongbum Cho and Hyeran Byun and Jisang Yoo",

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Lee, HS, Cho, Y, Byun, H & Yoo, J 2000, An image enhancement technique based on wavelets. in T Poggio, S-W Lee & HH Bulthoff (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1811, Springer Verlag, pp. 286-296, 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000, Seoul, Korea, Republic of, 00/5/15. https://doi.org/10.1007/3-540-45482-9_28

An image enhancement technique based on wavelets. / Lee, Hae Sung; Cho, Yongbum; Byun, Hyeran et al.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Tomaso Poggio; Seong-Whan Lee; Heinrich H. Bulthoff. Springer Verlag, 2000. p. 286-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811).

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

TY - GEN

T1 - An image enhancement technique based on wavelets

AU - Lee, Hae Sung

AU - Cho, Yongbum

AU - Byun, Hyeran

AU - Yoo, Jisang

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - We propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, a frame wavelet system designed as an optimal edge detector is used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm is tested on three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm is better than those of other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system. The experimental results also show that our algorithm has approximately same capability of deblocking as those of previous developed techniques.

AB - We propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, a frame wavelet system designed as an optimal edge detector is used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm is tested on three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm is better than those of other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system. The experimental results also show that our algorithm has approximately same capability of deblocking as those of previous developed techniques.

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SN - 3540675604

SN - 9783540675600

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

T2 - 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000

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ER -

Lee HS, Cho Y, Byun H, Yoo J. An image enhancement technique based on wavelets. In Poggio T, Lee S-W, Bulthoff HH, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2000. p. 286-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45482-9_28

An image enhancement technique based on wavelets

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