httomolibgpu.misc.denoise

`httomolibgpu.misc.denoise`#

Module for data denoising. For more detailed information see Data denoising module.

httomolibgpu.misc.denoise.total_variation_PD(data: <cp.ndarray>, regularisation_parameter: float | None = 1e-05, iterations: int | None = 1000, isotropic: bool | None = True, nonnegativity: bool | None = False, lipschitz_const: float | None = 8.0, gpu_id: int | None = 0) → <cp.ndarray>[source]#

Primal Dual algorithm for non-smooth convex Total Variation functional [2]. See more in Total Variation denoising (PD).

Parameters:

data (cp.ndarray) – Input CuPy 3D array of float32 data type.
regularisation_parameter (float) – Regularisation parameter to control the level of smoothing. Defaults to 1e-05.
iterations (int) – The number of iterations. Defaults to 1000.
isotropic (bool) – Choose between isotropic or anisotropic TV norm. Defaults to isotropic.
nonnegativity (bool) – Enable non-negativity in iterations. Defaults to False.
lipschitz_const (float) – Lipschitz constant to control convergence. Defaults to 8.
gpu_id (int) – GPU device index to perform processing on. Defaults to 0.

Returns:

TV-PD filtered 3D CuPy array in float32 data type.

Return type:

ndarray

Raises:

ValueError – If the input array is not float32 data type.

httomolibgpu.misc.denoise.total_variation_ROF(data: <cp.ndarray>, regularisation_parameter: float | None = 1e-05, iterations: int | None = 3000, time_marching_parameter: float | None = 0.001, gpu_id: int | None = 0) → <cp.ndarray>[source]#

Total Variation using Rudin-Osher-Fatemi (ROF) [8] explicit iteration scheme to perform edge-preserving image denoising. This is a gradient-based algorithm for a smoothed TV term which requires a small time marching parameter and a significant number of iterations. See more in Total Variation denoising (ROF).