The application of Markov random field models to wavelet-based image denoising
Imaging and vision systems
Choice of wavelet smoothness, primary resolution and threshold in wavelet shrinkage
Statistics and Computing
Image denoising using self-organizing map-based nonlinear independent component analysis
Neural Networks - New developments in self-organizing maps
IEEE Transactions on Image Processing
Multilevel threshold based image denoising in curvelet domain
Journal of Computer Science and Technology
Image denoising based on the ridgelet frame using the generalized cross validation technique
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We present a denoising method based on wavelets and generalized cross validation and apply these methods to image denoising. We describe the method of modified wavelet reconstruction and show that the related shrinkage parameter vector can be chosen without prior knowledge of the noise variance by using the method of generalized cross validation. By doing so, we obtain an estimate of the shrinkage parameter vector and, hence, the image, which is very close to the best achievable mean-squared error result-that given by complete knowledge of the underlying clean image