nanopyx.methods.restoration.denoising

 1import numpy as np
 2from ...core.transform._le_nlm_denoising import NLMDenoising
 3
 4
 5def non_local_means_denoising(
 6    img: np.ndarray,
 7    patch_size: int = 7,
 8    patch_distance: int = 11,
 9    h: float = 0.1,
10    sigma: float = 0.0,
11):
12    """
13    Apply Non-Local Means (NLM) denoising algorithm to an image.
14
15    Parameters
16    ----------
17    img : np.ndarray
18        The input image as a 2D numpy array.
19    patch_size : int, optional
20        The size of the square patch used for denoising. Default is 7.
21    patch_distance : int, optional
22        The maximum distance between any two patches used for denoising. Default is 11.
23    h : float, optional
24        The filtering parameter controlling the degree of smoothing. Higher values increase smoothing. Default is 0.1.
25    sigma : float, optional
26        The standard deviation of the noise (if known). Default is 0.0, please estimate the value on your image with np.float32 dtype. For example, using np.std(img.astype(np.float32)).
27
28    Returns
29    -------
30    np.ndarray
31        The denoised image as a 2D numpy array.
32
33    Notes
34    -----
35    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.
36    """
37    denoiser = NLMDenoising()
38    return denoiser.run(
39        img,
40        patch_size=patch_size,
41        patch_distance=patch_distance,
42        h=h,
43        sigma=sigma,
44    )