nanopyx.methods.restoration.denoising

 1import numpy as np
 2from ...core.transform._le_nlm_denoising import NLMDenoising
 3
 4
 5def non_local_means_denoising(img: np.ndarray, patch_size: int = 7, patch_distance: int = 11, h: float = 0.1, sigma: float = 0.0):
 6    """
 7    Apply Non-Local Means (NLM) denoising algorithm to an image.
 8
 9    Parameters
10    ----------
11    img : np.ndarray
12        The input image as a 2D numpy array.
13    patch_size : int, optional
14        The size of the square patch used for denoising. Default is 7.
15    patch_distance : int, optional
16        The maximum distance between any two patches used for denoising. Default is 11.
17    h : float, optional
18        The filtering parameter controlling the degree of smoothing. Higher values increase smoothing. Default is 0.1.
19    sigma : float, optional
20        The standard deviation of the noise (if known). Default is 0.0, which means it is estimated from the image.
21
22    Returns
23    -------
24    np.ndarray
25        The denoised image as a 2D numpy array.
26
27    Notes
28    -----
29    The Non-Local Means algorithm denoises an image by replacing each pixel's value with an average of similar pixels in a local neighborhood. This method is particularly effective for preserving edges and fine details in images.
30    """
31    denoiser = NLMDenoising()
32    return denoiser.run(img, patch_size=patch_size, patch_distance=patch_distance, h=h, sigma=sigma)