from functools import partial
from typing import Optional, List, Tuple
import numpy
from numpy.typing import ArrayLike
from skimage.restoration import denoise_bilateral as skimage_denoise_bilateral
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_bilateral(
image: ArrayLike,
bins: int = 10000,
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 bilateral denoiser for the given image and returns the optimal
parameters obtained using the N2S loss.
Note: it seems that the bilateral filter of scikit-image
is broken!
Parameters
----------
image: ArrayLike
Image to calibrate denoiser for.
bins: int
Number of discrete values for Gaussian weights of
color filtering. A larger value results in improved
accuracy.
(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
)
# Parameters to test when calibrating the denoising algorithm
parameter_ranges = {'sigma_spatial': (0.01, 1), 'sigma_color': (0.01, 1)}
# Combine fixed parameters:
other_fixed_parameters = other_fixed_parameters | {'bins': bins}
# Partial function:
_denoise_bilateral = partial(denoise_bilateral, **other_fixed_parameters)
# Calibrate denoiser
best_parameters = (
calibrate_denoiser(
crop,
_denoise_bilateral,
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 = 2 * image.nbytes
return denoise_bilateral, best_parameters, memory_needed
[docs]def denoise_bilateral(
image: ArrayLike,
sigma_color: Optional[float] = None,
sigma_spatial: float = 1,
bins: int = 10000,
**kwargs,
):
"""
Denoises the given image using a <a
href="https://en.wikipedia.org/wiki/Bilateral_filter">bilateral
filter</a>.
The bilateral filter is a edge-preserving smoothing filter that can
be used for image denoising. Each pixel value is replaced by a
weighted average of intensity values from nearby pixels. The
weighting is inversely related to the pixel distance in space but
also in the pixels value differences.
Parameters
----------
image : ArrayLike
Image to denoise
sigma_color : float
Standard deviation for grayvalue/color distance (radiometric
similarity). A larger value results in averaging of pixels with larger
radiometric differences. Note, that the image will be converted using
the `img_as_float` function and thus the standard deviation is in
respect to the range ``[0, 1]``. If the value is ``None`` the standard
deviation of the ``image`` will be used.
sigma_spatial : float
Standard deviation for range distance. A larger value results in
averaging of pixels with larger spatial differences.
bins : int
Number of discrete values for Gaussian weights of color filtering.
A larger value results in improved accuracy.
kwargs: dict
Other parameters
Returns
-------
Denoised image
"""
# Convert image to float if needed:
image = image.astype(dtype=numpy.float32, copy=False)
_skimage_denoise_bilateral = extend_nd(available_dims=[2])(
skimage_denoise_bilateral
)
return _skimage_denoise_bilateral(
image,
sigma_color=sigma_color,
sigma_spatial=sigma_spatial,
bins=bins,
mode='reflect',
**kwargs,
)