IEEE Spectrum
The what, how, and why of wavelet shrinkage denoising
Computing in Science and Engineering
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Optimal wavelet thresholding for various coding schemes
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
The discrete multiple wavelet transform and thresholding methods
IEEE Transactions on Signal Processing
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Spatially adaptive wavelet-based multiscale image restoration
IEEE Transactions on Image Processing
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Scale selection for anisotropic diffusion filter by Markov random field model
Pattern Recognition
A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands
IEEE Transactions on Signal Processing
Robust noise estimation based on noise injection
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Image-pair-based deblurring with spatially varying norms and noisy image updating
Journal of Visual Communication and Image Representation
Robust Noise Estimation Based on Noise Injection
Journal of Signal Processing Systems
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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.