Image denoising with an optimal threshold and neighbouring window
Pattern Recognition Letters
A Fast Scheme for Multiscale Signal Denoising
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
Foundations and Trends in Signal Processing
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Fast interscale wavelet denoising of Poisson-corrupted images
Signal Processing
Finite element techniques for removing the mixture of Gaussian and impulsive noise
IEEE Transactions on Signal Processing
Generalized SURE for exponential families: applications to regularization
IEEE Transactions on Signal Processing
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Removal of correlated noise by modeling the signal of interest in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Restoration of images corrupted by Gaussian and uniform impulsive noise
Pattern Recognition
A new denoising system for SONAR images
Journal on Image and Video Processing
Smooth adaptation by sigmoid shrinkage
Journal on Image and Video Processing
Covariance estimation in decomposable Gaussian graphical models
IEEE Transactions on Signal Processing
Expert Systems with Applications: An International Journal
A wavelet-based image denoising using least squares support vector machine
Engineering Applications of Artificial Intelligence
Noiseless codelength in wavelet denoising
EURASIP Journal on Advances in Signal Processing
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
AntShrink: Ant colony optimization for image shrinkage
Pattern Recognition Letters
Journal of Mathematical Imaging and Vision
A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context
WSEAS Transactions on Signal Processing
Stochastic image denoising based on Markov-chain Monte Carlo sampling
Signal Processing
Image denoising with anisotropic bivariate shrinkage
Signal Processing
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Adaptive noise reduction of scintigrams with a wavelet transform
Journal of Biomedical Imaging
Full length article: Emerging applications of wavelets: A review
Physical Communication
Time-Scale Similarities for Robust Image De-noising
Journal of Mathematical Imaging and Vision
Gabor feature based nonlocal means filter for textured image denoising
Journal of Visual Communication and Image Representation
Edge structure preserving image denoising using OAGSM/NC statistical model
Digital Signal Processing
Gradient-based Wiener filter for image denoising
Computers and Electrical Engineering
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
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This paper introduces a new approach to orthonormal wavelet image denoising. Instead of postulating a statistical model for the wavelet coefficients, we directly parametrize the denoising process as a sum of elementary nonlinear processes with unknown weights. We then minimize an estimate of the mean square error between the clean image and the denoised one. The key point is that we have at our disposal a very accurate, statistically unbiased, MSE estimate-Stein's unbiased risk estimate-that depends on the noisy image alone, not on the clean one. Like the MSE, this estimate is quadratic in the unknown weights, and its minimization amounts to solving a linear system of equations. The existence of this a priori estimate makes it unnecessary to devise a specific statistical model for the wavelet coefficients. Instead, and contrary to the custom in the literature, these coefficients are not considered random any more. We describe an interscale orthonormal wavelet thresholding algorithm based on this new approach and show its near-optimal performance-both regarding quality and CPU requirement-by comparing it with the results of three state-of-the-art nonredundant denoising algorithms on a large set of test images. An interesting fallout of this study is the development of a new, group-delay-based, parent-child prediction in a wavelet dyadic tree