A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
DCC '97 Proceedings of the Conference on Data Compression
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
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This paper presents an image denoising method based on a two-step empirical Bayes approach. A linear minimum mean squared error-like estimation is performed to estimate the wavelet coefficients of the denoised image. These coefficients rely on a suitable estimation of the variance of the wavelet coefficients for the "clean" image. The later uses maximum likelihood estimation over a local neighborhood. As opposed to the approach presented in [3], the estimation of the variance of the coefficients for the "clean" image is performed only at locations corresponding to father and descendant wavelet coefficients greater than a threshold T. Thus, the proposed method is based on a mixed local-global criterion in the wavelet domain and the results achieved are among the best reported in the literature.