Improved estimation of clutter properties in speckled imagery
Computational Statistics & Data Analysis
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Processing of Synthetic Aperture Radar (SAR) Images
Processing of Synthetic Aperture Radar (SAR) Images
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
IEEE Transactions on Image Processing
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
Journal of Mathematical Imaging and Vision
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Multiplicative noise removal using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Dealing with monotone likelihood in a model for speckled data
Computational Statistics & Data Analysis
Nonlocal filters for removing multiplicative noise
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion
IEEE Transactions on Image Processing
Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising
IEEE Transactions on Image Processing
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
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A new similarity measure and nonlocal filters for images corrupted by multiplicative noise are presented. The considered filters are generalizations of the nonlocal means filter of Buades et al., which is known to be well suited for removing additive Gaussian noise. To adapt this filter to different noise models, the involved patch comparison has first of all to be performed by a suitable noise dependent similarity measure. For this purpose, a recently proposed probabilistic measure for general noise models by Deledalle et al. is studied. This measure is analyzed in the context of conditional density functions and its properties are examined for data corrupted by additive and multiplicative noise. Since it turns out to have unfavorable properties for multiplicative noise, a new similarity measure is deduced consisting of a probability density function specially chosen for this type of noise. The properties of this new measure are studied theoretically as well as by numerical experiments. To finally obtain nonlocal filters, a weighted maximum likelihood estimation framework is applied, which also incorporates the noise statistics. Moreover, the weights occurring in these filters are defined using the new similarity measure and different adaptations are proposed to further improve the results. Finally, restoration results for images corrupted by multiplicative Gamma and Rayleigh noise are presented to demonstrate the very good performance of these nonlocal filters.