An innovative approach for spatial video noise reduction using wavelet based frequency decomposition
An innovative approach for spatial video noise reduction using wavelet based frequency decomposition
Many real word images are contaminated by noise. The noise not only degrades image quality but may also hinder further processing operations. Noise reduction techniques aim to both improve image quality and to aid further image processing. Spatial noise reduction techniques based on the discrete wavelet transform have been widely researched. This paper considers an undecimated shift invariant filter bank that has been used to decompose the image into components. The basic filters are derived from a biorthogonal wavelet basis. Reconstruction is obtained by a simple summation of the image components. A new thresholding scheme, which is obtained from Bayesian estimator theory, is used. The threshold parameters for each component are dependent on the noise level and are selected using a preliminary training procedure. The cost function utilised for the training is a weighted version of the mean square error which is designed to reflect human perception. The method compares favourably with other wavelet based noise reduction techniques and demonstrates significant noise reduction and visual quality enhancement.
281-284
De Stefano, A.
103547f3-163d-4670-8839-1799a638e653
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Collis, W.B.
961c2de6-3be9-419e-b2b5-b21bc9126598
September 2000
De Stefano, A.
103547f3-163d-4670-8839-1799a638e653
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Collis, W.B.
961c2de6-3be9-419e-b2b5-b21bc9126598
De Stefano, A., White, P.R. and Collis, W.B.
(2000)
An innovative approach for spatial video noise reduction using wavelet based frequency decomposition.
In Proceedings. 2000 International Conference on Image Processing, 2000.
IEEE.
.
(doi:10.1109/ICIP.2000.899350).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Many real word images are contaminated by noise. The noise not only degrades image quality but may also hinder further processing operations. Noise reduction techniques aim to both improve image quality and to aid further image processing. Spatial noise reduction techniques based on the discrete wavelet transform have been widely researched. This paper considers an undecimated shift invariant filter bank that has been used to decompose the image into components. The basic filters are derived from a biorthogonal wavelet basis. Reconstruction is obtained by a simple summation of the image components. A new thresholding scheme, which is obtained from Bayesian estimator theory, is used. The threshold parameters for each component are dependent on the noise level and are selected using a preliminary training procedure. The cost function utilised for the training is a weighted version of the mean square error which is designed to reflect human perception. The method compares favourably with other wavelet based noise reduction techniques and demonstrates significant noise reduction and visual quality enhancement.
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Published date: September 2000
Venue - Dates:
2000 International Conference on Image Processing, 2000, Vancouver, Canada, 2000-09-10 - 2000-09-13
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Local EPrints ID: 10775
URI: http://eprints.soton.ac.uk/id/eprint/10775
PURE UUID: ae615d0a-f2c3-41c3-b805-cbad47174a69
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Date deposited: 15 Jul 2005
Last modified: 16 Mar 2024 02:39
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Author:
A. De Stefano
Author:
W.B. Collis
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