A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to wavelets
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Filtering and deconvolution by the wavelet transform
Signal Processing
Wavelets and subband coding
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
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In recent years wavelet transforms have been widely used for image denoising. This is because wavelet transform represents both the stationary and the transient behavior of the image. In this paper an adaptive filtering method is used for removing additive white Gaussian noise. It is based on statistics estimated from a local neighborhood of each wavelet coefficient. Denoising results compare favorably to the shrinkage denoising method, both perceptually and in terms of signal to noise ratio (SNR). The performance of the method is compared to shrinkage denoising method for both low and high (SNR) images.