Implements a denoising filter that uses iterative non-local, or semi-local, weighted averaging of image patches for image denoising. The intensity at each pixel ‘p’ gets updated as a weighted average of intensities of a chosen subset of pixels from the image.
Type: Image4DFloat, Required, Single
Values: NoModel, Gaussian, Rician, Poisson
Noise Sigma Number
Noise std. dev.
Fidelity Weight Number
Set the fidelity weight. This weight prevents large deviations of the denoised image from the noisy data.
Kernel Bandwidth Estimation
Use Estimation Boolean
If TRUE, the kernal bandwith will be estimated automatically.
Fraction Pixels Number
Set the fraction of voxels in the image that will be used for kernel bandwidth sigma estimation. To reduce the computational burden for computing sigma, a small random fraction of the image pixels can be used.
Multiplication Factor Number
Set the kernel bandwidth sigma multiplication factor used to modify the automatically-estimated kernel bandwidth sigma. At times, it may be desirable to modify the value of the automatically-estimated sigma. Typically, this number isn't very far from 1. Note: This is used only when Use Estimation is TRUE.
Kernel Sigma Number
Set initial kernel bandwidth estimate. Note: This is changed when Use Estimation is TRUE.
Update Frequencey Integer
Set the update frequency. An optimal bandwidth will be re-estimated based on the denoised image after every ‘n’ iterations. Defaults to 3, i.e. bandwidth updated after every 3 denoising iteration.
Number Of Iterations Integer
Set the number of denoising iterations to perform. Defaults to 3.
Number Of Sample Patches Integer
Set the number of patches to sample for each pixel.
Patch Radius Integer
Set the patch radius specified in physical coordinates. Patch radius is preferably set to an even number. Currently, only isotropic patches in physical space are allowed; patches can be anisotropic in voxel space.
Sample Varience Number
Set the variance of the domain where patches are sampled.
Keywords: patch, denoising, noise
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