Loading...
Search for:
redundant-representation
0.007 seconds
Image denoising using sparse representations
, Article 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, Paraty, 15 March 2009 through 18 March 2009 ; Volume 5441 , 2009 , Pages 557-564 ; 03029743 (ISSN) ; Firouzi, H ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
The problem of removing white zero-mean Gaussian noise from an image is an interesting inverse problem to be investigated in this paper through sparse and redundant representations. However, finding the sparsest possible solution in the noise scenario was of great debate among the researchers. In this paper we make use of new approach to solve this problem and show that it is comparable with the state-of-art denoising approaches. © Springer-Verlag Berlin Heidelberg 2009
An adaptive thresholding approach for image denoising using redundant representations
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009, Grenoble ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Abstract
A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. Although the use of shrinkage is optimal for Gaussian white noise with complete and unitary transforms, it has already been shown that shrinkage has promising results even with redundant transforms. In this paper, we propose using adaptive thresholding of redundant representations of the noisy image for image denoising. In the proposed thresholding scheme, a different threshold is used for each representation coefficient of the noisy image in an overcomplete transform. In this method, each threshold is automatically set based on statistical properties of the...