patch_denoise.denoise.optimal_thresholding#

patch_denoise.denoise.optimal_thresholding(input_data, patch_shape, patch_overlap, mask=None, mask_threshold=50, loss='fro', noise_std=None, recombination='weighted', eps_marshenko_pastur=1e-07, progbar=None)[source]#

Optimal thresholing denoising method.

Parameters:

input_data (numpy.ndarray) – The input data to denoise. It should be a ND array, and the last dimension should a dynamically varying one (eg time).
progbar (tqdm.tqdm Progress bar, optiononal) – An existing Progressbar, default (None) will create a new one.
patch_shape (tuple) – The patch shape
patch_overlap (tuple) – the overlap of each pixel
recombination (str, optional) – The recombination method of the patch. “weighted”, “average” or “center”. default “weighted”.
mask (numpy.ndarray) – A boolean array, defining a ROI in the volume. Only patch with voxels in the ROI will be processed.
mask_threshold (int) – percentage of the path that has to be in the mask so that the patch is processed. if mask_threshold = -1, all the patch are processed, if mask_threshold=100, all the voxels of the patch needs to be in the mask
noise_std (float or numpy.ndarray) – An estimation of the spatial noise map standard deviation.
loss (str) – The loss for which the optimal thresholding is perform.
eps_marshenko_pastur (float) – The precision with which the optimal threshold is computed.

Returns:

numpy.ndarray: The denoised sequence of volume numpy.ndarray: The weight of each pixel after the processing. numpy.ndarray: If possible, the noise variance distribution in the volume numpy.ndarray: If possible, the rank of each patch in the volume.

Return type:

tuple

Notes

Reimplement of the original Matlab code [1] in python.

References