Statistical distributions of image DCT coefficients
Computers and Electrical Engineering
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Signal Processing with Lapped Transforms
Signal Processing with Lapped Transforms
Fast Progressive Image Coding without Wavelets
DCC '00 Proceedings of the Conference on Data Compression
Distributions of 3D DCT coefficients for video
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Spatially Adaptive Wavelet-Based Method Using the Cauchy Prior for Denoising the SAR Images
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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We introduce a new image denoising method based on the statistical modelling of dyadic rearranged lapped transform LT coefficients. Based on Kolomogrov-Smirnov KS goodness of fit test, we have shown that the statistical distribution of the dyadic rearranged LT coefficients in a subband is best approximated by the generalised Gaussian distribution. A Bayesian minimum mean square error MMSE estimator is used to obtain the estimate of noise free coefficients, which is based on modelling the global distribution of the dyadic rearranged LT coefficients using generalised Gaussian distribution. The LT-based image denoising method with generalised Gaussian prior shows highly encouraging both objective and subjective results when compared to several well-known image denoising methods.