patch_denoise.denoise.adaptive_thresholding#

patch_denoise.denoise.adaptive_thresholding(input_data, patch_shape, patch_overlap, mask=None, mask_threshold=50, recombination='weighted', method='SURE', nbsim=500, tau0=None, gamma0=None, noise_std=1.0, 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.

noise_std: float or numpy.ndarray: An estimation of the spatial noise map standard deviation.
method: str: The adaptive method to use “SURE” or “GSURE”
nbsim:: Number of simulation for computing sure estimator
tau:: Simulation parameter.
gamma0:: Simulation parameter.

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

Reimplements the R package [1] in python.

References