from functools import partial
from typing import Optional, Tuple, List
import numpy
from numpy.typing import ArrayLike
from skimage.restoration import denoise_nl_means as skimage_denoise_nl_means
from skimage.restoration import estimate_sigma
from aydin.it.classic_denoisers import _defaults
from aydin.util.crop.rep_crop import representative_crop
from aydin.util.denoise_nd.denoise_nd import extend_nd
from aydin.util.j_invariance.j_invariance import calibrate_denoiser
[docs]def calibrate_denoise_nlm(
image: ArrayLike,
patch_size: int = 7,
patch_distance: int = 11,
crop_size_in_voxels: Optional[int] = _defaults.default_crop_size_normal.value,
optimiser: str = _defaults.default_optimiser.value,
max_num_evaluations: int = _defaults.default_max_evals_normal.value,
blind_spots: Optional[List[Tuple[int]]] = _defaults.default_blind_spots.value,
jinv_interpolation_mode: str = _defaults.default_jinv_interpolation_mode.value,
display_images: bool = False,
display_crop: bool = False,
**other_fixed_parameters,
):
"""
Calibrates the Non-Local Means (NLM) denoiser for the given image and
returns the optimal parameters obtained using the N2S loss.
Parameters
----------
image: ArrayLike
Image to calibrate denoiser for.
patch_size : int, optional
Size of patches used for denoising.
(advanced)
patch_distance : int, optional
Maximal distance in pixels where to search patches used for denoising.
(advanced)
crop_size_in_voxels: int or None for default
Number of voxels for crop used to calibrate denoiser.
Increase this number by factors of two if denoising quality is
unsatisfactory -- this can be important for very noisy images.
Values to try are: 65000, 128000, 256000, 320000.
We do not recommend values higher than 512000.
optimiser: str
Optimiser to use for finding the best denoising
parameters. Can be: 'smart' (default), or 'fast' for a mix of SHGO
followed by L-BFGS-B.
(advanced)
max_num_evaluations: int
Maximum number of evaluations for finding the optimal parameters.
Increase this number by factors of two if denoising quality is
unsatisfactory.
blind_spots: bool
List of voxel coordinates (relative to receptive field center) to
be included in the blind-spot. For example, you can give a list of
3 tuples: [(0,0,0), (0,1,0), (0,-1,0)] to extend the blind spot
to cover voxels of relative coordinates: (0,0,0),(0,1,0), and (0,-1,0)
(advanced) (hidden)
jinv_interpolation_mode: str
J-invariance interpolation mode for masking. Can be: 'median' or
'gaussian'.
(advanced)
display_images: bool
When True the denoised images encountered during optimisation are shown.
(advanced) (hidden)
display_crop: bool
Displays crop, for debugging purposes...
(advanced) (hidden)
other_fixed_parameters: dict
Any other fixed parameters
Returns
-------
Denoising function, dictionary containing optimal parameters,
and free memory needed in bytes for computation.
"""
# Convert image to float if needed:
image = image.astype(dtype=numpy.float32, copy=False)
# obtain representative crop, to speed things up...
crop = representative_crop(
image, crop_size=crop_size_in_voxels, display_crop=display_crop
)
# We make a first estimate of the noise sigma:
estimated_sigma = estimate_sigma(image)
sigma_range = (max(0.0, 0.2 * estimated_sigma), 4 * estimated_sigma)
cutoff_distance_range = (max(0.0, 0.2 * estimated_sigma), 4 * estimated_sigma)
# Parameters to test when calibrating the denoising algorithm
parameter_ranges = {'sigma': sigma_range, 'cutoff_distance': cutoff_distance_range}
# Combine fixed parameters:
other_fixed_parameters = other_fixed_parameters | {
'patch_size': patch_size,
'patch_distance': patch_distance,
}
# Partial function:
_denoise_nl_means = partial(denoise_nlm, **other_fixed_parameters)
# Calibrate denoiser
best_parameters = (
calibrate_denoiser(
crop,
_denoise_nl_means,
mode=optimiser,
denoise_parameters=parameter_ranges,
interpolation_mode=jinv_interpolation_mode,
max_num_evaluations=max_num_evaluations,
blind_spots=blind_spots,
display_images=display_images,
)
| other_fixed_parameters
)
# Memory needed:
memory_needed = 3 * image.nbytes
return denoise_nlm, best_parameters, memory_needed
[docs]def denoise_nlm(
image: ArrayLike,
patch_size: int = 7,
patch_distance: int = 11,
cutoff_distance: float = 0.1,
sigma=0.0,
):
"""
Denoise given image using either scikit-image implementation
of <a href="https://en.wikipedia.org/wiki/Non-local_means">Non-Local-Means (NLM)</a>.
Parameters
----------
image : ArayLike
Image to be denoised
patch_size : int, optional
Size of patches used for denoising.
patch_distance : int, optional
Maximal distance in pixels where to search patches used for denoising.
cutoff_distance : float, optional
Cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches. A higher h results in a smoother image,
at the expense of blurring features. For a Gaussian noise of standard
deviation sigma, a rule of thumb is to choose the value of h to be
sigma of slightly less.
sigma : float, optional
The standard deviation of the (Gaussian) noise. If provided, a more
robust computation of patch weights is computed that takes the expected
noise variance into account (see Notes below).
Returns
-------
Denoised image as ndarray.
"""
# Convert image to float if needed:
image = image.astype(dtype=numpy.float32, copy=False)
# Make it work for nD images:
_skimage_denoise_nl_means = extend_nd(available_dims=[2, 3])(
skimage_denoise_nl_means
)
return _skimage_denoise_nl_means(
image,
patch_size=patch_size,
patch_distance=patch_distance,
h=cutoff_distance,
sigma=sigma,
)