skimage2.restoration.calibrate_denoiser#

skimage2.restoration.calibrate_denoiser(image, denoise_function, denoise_parameters, *, stride=4, approximate_loss=True, extra_output=False)[source]#

Calibrate a denoising function and return optimal J-invariant version.

The returned function is partially evaluated with optimal parameter values set for denoising the input image.

Parameters:

imagendarray: Input data to be denoised (converted using img_as_float).
denoise_functionfunction: Denoising function to be calibrated.
denoise_parametersdict of list: Ranges of parameters for denoise_function to be calibrated over.
strideint, optional: Stride used in masking procedure that converts denoise_function to J-invariance.
approximate_lossbool, optional: Whether to approximate the self-supervised loss used to evaluate the denoiser by only computing it on one masked version of the image. If False, the runtime will be a factor of stride**image.ndim longer.
extra_outputbool, optional: If True, return parameters and losses in addition to the calibrated denoising function

Returns:

best_denoise_functionfunction: The optimal J-invariant version of denoise_function.
If extra_output is True, the following tuple is also returned:
(parameters_tested, losses)tuple (list of dict, list of int): List of parameters tested for denoise_function, as a dictionary of kwargs Self-supervised loss for each set of parameters in parameters_tested.

Notes

The calibration procedure uses a self-supervised mean-square-error loss to evaluate the performance of J-invariant versions of denoise_function. The minimizer of the self-supervised loss is also the minimizer of the ground-truth loss (i.e., the true MSE error) [1]. The returned function can be used on the original noisy image, or other images with similar characteristics.

Increasing the stride increases the performance of best_denoise_function: at the expense of increasing its runtime. It has no effect on the runtime of the calibration.

References

[1]

J. Batson & L. Royer. Noise2Self: Blind Denoising by Self-Supervision, International Conference on Machine Learning, p. 524-533 (2019).

Examples

>>> from _skimage2 import color, data
>>> from _skimage2.restoration import denoise_tv_chambolle
>>> import numpy as np
>>> img = color.rgb2gray(data.astronaut()[:50, :50])
>>> rng = np.random.default_rng()
>>> noisy = img + 0.5 * img.std() * rng.standard_normal(img.shape)
>>> parameters = {'weight': np.arange(0.01, 0.3, 0.02)}
>>> denoising_function = calibrate_denoiser(noisy, denoise_tv_chambolle,
...                                         denoise_parameters=parameters)
>>> denoised_img = denoising_function(img)