Hidden Markov tree modeling of complex wavelet transforms
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
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
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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Several methods have been developed to enhance the performance of image denoising algorithms. In this letter we have developed an algorithm for image noise removal based on local adaptive window size/shape filtering. While rectangular windows are efficient, they yield poor results near object boundaries. We describe an efficient method for determining the variable size of the locally adaptive window using a region-based approach. A region including a denoising point is partitioned into disjoint subregions. The locally adaptive window for denoising is obtained by selecting the proper subregions. Our approach can be applied to several problems, including image restoration and visual correspondence. Comparison of the algorithm with the known techniques for noise removal from images shows the advantage of the new algorithm, both quantitatively and visually.