![]() ![]() imshow ( get_inset ( denoised_calibrated_wavelet ), cmap = 'Greys_r' ) axes. imshow ( get_inset ( denoised_default_wavelet ), cmap = 'Greys_r' ) axes. set_title ( 'NL Means Calibrated' ) axes. imshow ( get_inset ( denoised_calibrated_nl ), cmap = 'Greys_r' ) axes. ![]() imshow ( get_inset ( denoised_default_nl ), cmap = 'Greys_r' ) axes. subplots ( ncols = 3, nrows = 2, figsize = ( 15, 8 )) for ax in axes. title ( 'Noisy Image' ) get_inset = lambda x : x fig, axes = plt. Import numpy as np from matplotlib import pyplot as plt from matplotlib import gridspec from skimage.data import chelsea, hubble_deep_field from trics import mean_squared_error as mse from trics import peak_signal_noise_ratio as psnr from skimage.restoration import ( calibrate_denoiser, denoise_wavelet, denoise_tv_chambolle, denoise_nl_means, estimate_sigma ) from skimage.util import img_as_float, random_noise from lor import rgb2gray from functools import partial _denoise_wavelet = partial ( denoise_wavelet, rescale_sigma = True ) image = img_as_float ( chelsea ()) sigma = 0.2 noisy = random_noise ( image, var = sigma ** 2 ) # Parameters to test when calibrating the denoising algorithm parameter_ranges = ' ) plt.
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