HybridPCADenoiser#

class patch_denoise.space_time.lowrank.HybridPCADenoiser(patch_shape, patch_overlap, recombination='weighted')[source]#

Denoising using the Hybrid-PCA thresholding method.

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 using the Hybrid-PCA method.

_patch_processing(patch, patch_idx=None, var_apriori=None)[source]#: Process a patch with the Hybrid-PCA method.

denoise(input_data, mask=None, mask_threshold=50, noise_std=1.0, progbar=None)[source]#

Denoise using the Hybrid-PCA method.

Along with the input data a noise std map or value should be provided.

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
noise_std: float or numpy.ndarray: An estimation of the spatial noise map standard deviation.

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