Image denoising with an optimal threshold and neighbouring window
Pattern Recognition Letters
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Signal Processing
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ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Nonparametric cepstrum estimation via optimal risk smoothing
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A new wavelet-based fuzzy single and multi-channel image denoising
Image and Vision Computing
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ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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Least squares estimation without priors or supervision
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SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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Journal of Biomedical Imaging
Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising
Engineering Applications of Artificial Intelligence
SURE-based optimization for adaptive sampling and reconstruction
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Dictionary learning and similarity regularization based image noise reduction
Journal of Visual Communication and Image Representation
The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules
International Journal of Healthcare Information Systems and Informatics
Multiple-step local Wiener filter with proper stopping in wavelet domain
Journal of Visual Communication and Image Representation
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We propose a new approach to image denoising, based on the image-domain minimization of an estimate of the mean squared error-Stein's unbiased risk estimate (SURE). Unlike most existing denoising algorithms, using the SURE makes it needless to hypothesize a statistical model for the noiseless image. A key point of our approach is that, although the (nonlinear) processing is performed in a transformed domain-typically, an undecimated discrete wavelet transform, but we also address nonorthonormal transforms-this minimization is performed in the image domain. Indeed, we demonstrate that, when the transform is a ldquotightrdquo frame (an undecimated wavelet transform using orthonormal filters), separate subband minimization yields substantially worse results. In order for our approach to be viable, we add another principle, that the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Armed with the SURE and LET principles, we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient. Quite remarkably, the very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential.