Training methods for image noise level estimation on wavelet components (in Special issue on Non-Linear Signal and Image Processing—Part II)
Training methods for image noise level estimation on wavelet components (in Special issue on Non-Linear Signal and Image Processing—Part II)
The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation (MAD). This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing, 13 and 5 images, respectively, are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered.
noise estimation, training methods, wavelet transform, image processing
2400-2407
De Stefano, A.
103547f3-163d-4670-8839-1799a638e653
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Collis, W.B.
961c2de6-3be9-419e-b2b5-b21bc9126598
January 2004
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.
(2004)
Training methods for image noise level estimation on wavelet components (in Special issue on Non-Linear Signal and Image Processing—Part II).
EURASIP Journal on Applied Signal Processing, 2004 (16), .
(doi:10.1155/S1110865704401218).
Abstract
The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation (MAD). This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing, 13 and 5 images, respectively, are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered.
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Published date: January 2004
Keywords:
noise estimation, training methods, wavelet transform, image processing
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Local EPrints ID: 11009
URI: http://eprints.soton.ac.uk/id/eprint/11009
ISSN: 0941-0635
PURE UUID: 196c73ca-8f22-46d9-89be-cd35672aef2e
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Date deposited: 15 Mar 2005
Last modified: 11 Jul 2024 01:33
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Author:
A. De Stefano
Author:
W.B. Collis
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