BaseSpaceTimeDenoiser

class patch_denoise.space_time.base.BaseSpaceTimeDenoiser(patch_shape, patch_overlap, recombination='weighted')

Bases: ABC

Base Class for Patch-based denoising methods for dynamical data.

Parameters:

• 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”.

Methods

__init__
denoise	Denoise the input_data, according to mask.

_get_patch_param(data_shape)

Return tuple for patch_shape and patch_overlap.

It works from whatever the input format was (int or list). This method also ensure that the patch will provide tall and skinny matrices.

abstract _patch_processing(patch, patch_slice=None, **kwargs)

Process a patch.

Implemented by child classes.

denoise(input_data, mask=None, mask_threshold=50, progbar=None)

Denoise the input_data, according to mask.

Patches are extracted sequentially and process by the implemented _patch_processing function. Only patches which have at least a voxel in the mask ROI are processed.

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.

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

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