import numpy as np from skimage import restoration, data, color, img_as_float, measure from skimage.measure import compare_psnr from skimage.restoration._denoise import _wavelet_threshold import pywt from skimage._shared import testing from skimage._shared.testing import (assert_equal, assert_almost_equal, assert_warns, assert_) from skimage._shared._warnings import expected_warnings from distutils.version import LooseVersion as Version if (Version(np.__version__) >= '1.15.0' and Version(pywt.__version__) <= '0.5.2'): PYWAVELET_ND_INDEXING_WARNING = 'non-tuple sequence for multidimensional' else: PYWAVELET_ND_INDEXING_WARNING = None np.random.seed(1234) astro = img_as_float(data.astronaut()[:128, :128]) astro_gray = color.rgb2gray(astro) checkerboard_gray = img_as_float(data.checkerboard()) checkerboard = color.gray2rgb(checkerboard_gray) def test_denoise_tv_chambolle_2d(): # astronaut image img = astro_gray.copy() # add noise to astronaut img += 0.5 * img.std() * np.random.rand(*img.shape) # clip noise so that it does not exceed allowed range for float images. img = np.clip(img, 0, 1) # denoise denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1) # which dtype? assert_(denoised_astro.dtype in [np.float, np.float32, np.float64]) from scipy import ndimage as ndi grad = ndi.morphological_gradient(img, size=((3, 3))) grad_denoised = ndi.morphological_gradient(denoised_astro, size=((3, 3))) # test if the total variation has decreased assert_(grad_denoised.dtype == np.float) assert_(np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum())) def test_denoise_tv_chambolle_multichannel(): denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1) denoised = restoration.denoise_tv_chambolle(astro, weight=0.1, multichannel=True) assert_equal(denoised[..., 0], denoised0) # tile astronaut subset to generate 3D+channels data astro3 = np.tile(astro[:64, :64, np.newaxis, :], [1, 1, 2, 1]) # modify along tiled dimension to give non-zero gradient on 3rd axis astro3[:, :, 0, :] = 2*astro3[:, :, 0, :] denoised0 = restoration.denoise_tv_chambolle(astro3[..., 0], weight=0.1) denoised = restoration.denoise_tv_chambolle(astro3, weight=0.1, multichannel=True) assert_equal(denoised[..., 0], denoised0) def test_denoise_tv_chambolle_float_result_range(): # astronaut image img = astro_gray int_astro = np.multiply(img, 255).astype(np.uint8) assert_(np.max(int_astro) > 1) denoised_int_astro = restoration.denoise_tv_chambolle(int_astro, weight=0.1) # test if the value range of output float data is within [0.0:1.0] assert_(denoised_int_astro.dtype == np.float) assert_(np.max(denoised_int_astro) <= 1.0) assert_(np.min(denoised_int_astro) >= 0.0) def test_denoise_tv_chambolle_3d(): """Apply the TV denoising algorithm on a 3D image representing a sphere.""" x, y, z = np.ogrid[0:40, 0:40, 0:40] mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 mask = 100 * mask.astype(np.float) mask += 60 mask += 20 * np.random.rand(*mask.shape) mask[mask < 0] = 0 mask[mask > 255] = 255 res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < mask.std()) def test_denoise_tv_chambolle_1d(): """Apply the TV denoising algorithm on a 1D sinusoid.""" x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000)) x += 20 * np.random.rand(x.size) x = np.clip(x, 0, 255) res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < x.std()) def test_denoise_tv_chambolle_4d(): """ TV denoising for a 4D input.""" im = 255 * np.random.rand(8, 8, 8, 8) res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < im.std()) def test_denoise_tv_chambolle_weighting(): # make sure a specified weight gives consistent results regardless of # the number of input image dimensions rstate = np.random.RandomState(1234) img2d = astro_gray.copy() img2d += 0.15 * rstate.standard_normal(img2d.shape) img2d = np.clip(img2d, 0, 1) # generate 4D image by tiling img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2)) w = 0.2 denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w) denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w) assert_(measure.compare_ssim(denoised_2d, denoised_4d[:, :, 0, 0]) > 0.99) def test_denoise_tv_bregman_2d(): img = checkerboard_gray.copy() # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_tv_bregman(img, weight=10) out2 = restoration.denoise_tv_bregman(img, weight=5) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_tv_bregman_float_result_range(): # astronaut image img = astro_gray.copy() int_astro = np.multiply(img, 255).astype(np.uint8) assert_(np.max(int_astro) > 1) denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0) # test if the value range of output float data is within [0.0:1.0] assert_(denoised_int_astro.dtype == np.float) assert_(np.max(denoised_int_astro) <= 1.0) assert_(np.min(denoised_int_astro) >= 0.0) def test_denoise_tv_bregman_3d(): img = checkerboard.copy() # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_tv_bregman(img, weight=10) out2 = restoration.denoise_tv_bregman(img, weight=5) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_2d(): img = checkerboard_gray.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=False) out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_spatial=20, multichannel=False) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_zeros(): img = np.zeros((10, 10)) assert_equal(img, restoration.denoise_bilateral(img, multichannel=False)) def test_denoise_bilateral_constant(): img = np.ones((10, 10)) * 5 assert_equal(img, restoration.denoise_bilateral(img, multichannel=False)) def test_denoise_bilateral_color(): img = checkerboard.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=True) out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_spatial=20, multichannel=True) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_3d_grayscale(): img = np.ones((50, 50, 3)) with testing.raises(ValueError): restoration.denoise_bilateral(img, multichannel=False) def test_denoise_bilateral_3d_multichannel(): img = np.ones((50, 50, 50)) with expected_warnings(["grayscale"]): result = restoration.denoise_bilateral(img, multichannel=True) assert_equal(result, img) def test_denoise_bilateral_multidimensional(): img = np.ones((10, 10, 10, 10)) with testing.raises(ValueError): restoration.denoise_bilateral(img, multichannel=False) with testing.raises(ValueError): restoration.denoise_bilateral(img, multichannel=True) def test_denoise_bilateral_nan(): import sys img = np.full((50, 50), np.NaN) # TODO: This warning is not optional in python3. This should be # made a strict warning when we get to 0.15 with expected_warnings(['invalid|\A\Z']): out = restoration.denoise_bilateral(img, multichannel=False) assert_equal(img, out) def test_denoise_nl_means_2d(): img = np.zeros((40, 40)) img[10:-10, 10:-10] = 1. sigma = 0.3 img += sigma * np.random.randn(*img.shape) for s in [sigma, 0]: denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True, multichannel=True, sigma=s) # make sure noise is reduced assert_(img.std() > denoised.std()) denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=False, multichannel=True, sigma=s) # make sure noise is reduced assert_(img.std() > denoised.std()) def test_denoise_nl_means_2d_multichannel(): # reduce image size because nl means is slow img = np.copy(astro[:50, :50]) img = np.concatenate((img, ) * 2, ) # 6 channels # add some random noise sigma = 0.1 imgn = img + sigma * np.random.standard_normal(img.shape) imgn = np.clip(imgn, 0, 1) for fast_mode in [True, False]: for s in [sigma, 0]: for n_channels in [2, 3, 6]: psnr_noisy = compare_psnr(img[..., :n_channels], imgn[..., :n_channels]) denoised = restoration.denoise_nl_means(imgn[..., :n_channels], 3, 5, h=0.75 * sigma, fast_mode=fast_mode, multichannel=True, sigma=s) psnr_denoised = compare_psnr(denoised[..., :n_channels], img[..., :n_channels]) # make sure noise is reduced assert_(psnr_denoised > psnr_noisy) def test_denoise_nl_means_3d(): img = np.zeros((12, 12, 8)) img[5:-5, 5:-5, 2:-2] = 1. sigma = 0.3 imgn = img + sigma * np.random.randn(*img.shape) psnr_noisy = compare_psnr(img, imgn) for s in [sigma, 0]: denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma, fast_mode=True, multichannel=False, sigma=s) # make sure noise is reduced assert_(compare_psnr(img, denoised) > psnr_noisy) denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma, fast_mode=False, multichannel=False, sigma=s) # make sure noise is reduced assert_(compare_psnr(img, denoised) > psnr_noisy) def test_denoise_nl_means_multichannel(): # for true 3D data, 3D denoising is better than denoising as 2D+channels img = np.zeros((13, 10, 8)) img[6, 4:6, 2:-2] = 1. sigma = 0.3 imgn = img + sigma * np.random.randn(*img.shape) denoised_wrong_multichannel = restoration.denoise_nl_means( imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=True) denoised_ok_multichannel = restoration.denoise_nl_means( imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=False) psnr_wrong = compare_psnr(img, denoised_wrong_multichannel) psnr_ok = compare_psnr(img, denoised_ok_multichannel) assert_(psnr_ok > psnr_wrong) def test_denoise_nl_means_wrong_dimension(): img = np.zeros((5, 5, 5, 5)) with testing.raises(NotImplementedError): restoration.denoise_nl_means(img, multichannel=True) def test_no_denoising_for_small_h(): img = np.zeros((40, 40)) img[10:-10, 10:-10] = 1. img += 0.3*np.random.randn(*img.shape) # very small h should result in no averaging with other patches denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=True, multichannel=True) assert_(np.allclose(denoised, img)) denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=False, multichannel=True) assert_(np.allclose(denoised, img)) def test_wavelet_denoising(): rstate = np.random.RandomState(1234) # version with one odd-sized dimension astro_gray_odd = astro_gray[:, :-1] astro_odd = astro[:, :-1] for img, multichannel, convert2ycbcr in [(astro_gray, False, False), (astro_gray_odd, False, False), (astro_odd, True, False), (astro_odd, True, True)]: sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # Verify that SNR is improved when true sigma is used with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): denoised = restoration.denoise_wavelet(noisy, sigma=sigma, multichannel=multichannel, convert2ycbcr=convert2ycbcr) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) # Verify that SNR is improved with internally estimated sigma with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): denoised = restoration.denoise_wavelet(noisy, multichannel=multichannel, convert2ycbcr=convert2ycbcr) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) # SNR is improved less with 1 wavelet level than with the default. denoised_1 = restoration.denoise_wavelet(noisy, multichannel=multichannel, wavelet_levels=1, convert2ycbcr=convert2ycbcr) psnr_denoised_1 = compare_psnr(img, denoised_1) assert_(psnr_denoised > psnr_denoised_1) assert_(psnr_denoised_1 > psnr_noisy) # Test changing noise_std (higher threshold, so less energy in signal) with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): res1 = restoration.denoise_wavelet(noisy, sigma=2 * sigma, multichannel=multichannel) with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): res2 = restoration.denoise_wavelet(noisy, sigma=sigma, multichannel=multichannel) assert_(np.sum(res1**2) <= np.sum(res2**2)) def test_wavelet_threshold(): rstate = np.random.RandomState(1234) img = astro_gray sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # employ a single, user-specified threshold instead of BayesShrink sigmas with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): denoised = _wavelet_threshold(noisy, wavelet='db1', method=None, threshold=sigma) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) # either method or threshold must be defined with testing.raises(ValueError): _wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None) # warns if a threshold is provided in a case where it would be ignored with expected_warnings(["Thresholding method ", PYWAVELET_ND_INDEXING_WARNING]): _wavelet_threshold(noisy, wavelet='db1', method='BayesShrink', threshold=sigma) def test_wavelet_denoising_nd(): rstate = np.random.RandomState(1234) for method in ['VisuShrink', 'BayesShrink']: for ndim in range(1, 5): # Generate a very simple test image if ndim < 3: img = 0.2*np.ones((128, )*ndim) else: img = 0.2*np.ones((16, )*ndim) img[(slice(5, 13), ) * ndim] = 0.8 sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # Mark H. 2018.08: # The issue arises because when ndim in [1, 2] # ``waverecn`` calls ``_match_coeff_dims`` # Which includes a numpy 1.15 deprecation. # for larger number of dimensions _match_coeff_dims isn't called # for some reason. anticipated_warnings = (PYWAVELET_ND_INDEXING_WARNING if ndim < 3 else None) with expected_warnings([anticipated_warnings]): # Verify that SNR is improved with internally estimated sigma denoised = restoration.denoise_wavelet(noisy, method=method) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) def test_wavelet_invalid_method(): with testing.raises(ValueError): restoration.denoise_wavelet(np.ones(16), method='Unimplemented') def test_wavelet_denoising_levels(): rstate = np.random.RandomState(1234) ndim = 2 N = 256 wavelet = 'db1' # Generate a very simple test image img = 0.2*np.ones((N, )*ndim) img[(slice(5, 13), ) * ndim] = 0.8 sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet) denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet, wavelet_levels=1) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) psnr_denoised_1 = compare_psnr(img, denoised_1) # multi-level case should outperform single level case assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy) # invalid number of wavelet levels results in a ValueError or UserWarning max_level = pywt.dwt_max_level(np.min(img.shape), pywt.Wavelet(wavelet).dec_len) if Version(pywt.__version__) < '1.0.0': # exceeding max_level raises a ValueError in PyWavelets 0.4-0.5.2 with testing.raises(ValueError): with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): restoration.denoise_wavelet( noisy, wavelet=wavelet, wavelet_levels=max_level + 1) else: # exceeding max_level raises a UserWarning in PyWavelets >= 1.0.0 with expected_warnings([ 'all coefficients will experience boundary effects']): restoration.denoise_wavelet( noisy, wavelet=wavelet, wavelet_levels=max_level + 1) with testing.raises(ValueError): with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): restoration.denoise_wavelet( noisy, wavelet=wavelet, wavelet_levels=-1) def test_estimate_sigma_gray(): rstate = np.random.RandomState(1234) # astronaut image img = astro_gray.copy() sigma = 0.1 # add noise to astronaut img += sigma * rstate.standard_normal(img.shape) sigma_est = restoration.estimate_sigma(img, multichannel=False) assert_almost_equal(sigma, sigma_est, decimal=2) def test_estimate_sigma_masked_image(): # Verify computation on an image with a large, noise-free border. # (zero regions will be masked out by _sigma_est_dwt to avoid returning # sigma = 0) rstate = np.random.RandomState(1234) # uniform image img = np.zeros((128, 128)) center_roi = (slice(32, 96), slice(32, 96)) img[center_roi] = 0.8 sigma = 0.1 img[center_roi] = sigma * rstate.standard_normal(img[center_roi].shape) sigma_est = restoration.estimate_sigma(img, multichannel=False) assert_almost_equal(sigma, sigma_est, decimal=1) def test_estimate_sigma_color(): rstate = np.random.RandomState(1234) # astronaut image img = astro.copy() sigma = 0.1 # add noise to astronaut img += sigma * rstate.standard_normal(img.shape) sigma_est = restoration.estimate_sigma(img, multichannel=True, average_sigmas=True) assert_almost_equal(sigma, sigma_est, decimal=2) sigma_list = restoration.estimate_sigma(img, multichannel=True, average_sigmas=False) assert_equal(len(sigma_list), img.shape[-1]) assert_almost_equal(sigma_list[0], sigma_est, decimal=2) # default multichannel=False should raise a warning about last axis size assert_warns(UserWarning, restoration.estimate_sigma, img) def test_wavelet_denoising_args(): """ Some of the functions inside wavelet denoising throw an error the wrong arguments are passed. This protects against that and verifies that all arguments can be passed. """ img = astro noisy = img.copy() + 0.1 * np.random.randn(*(img.shape)) for convert2ycbcr in [True, False]: for multichannel in [True, False]: anticipated_warnings = (PYWAVELET_ND_INDEXING_WARNING if multichannel else None) for sigma in [0.1, [0.1, 0.1, 0.1], None]: if (not multichannel and not convert2ycbcr) or \ (isinstance(sigma, list) and not multichannel): continue with expected_warnings([anticipated_warnings]): restoration.denoise_wavelet(noisy, sigma=sigma, convert2ycbcr=convert2ycbcr, multichannel=multichannel) def test_multichannel_warnings(): img = data.astronaut() assert_warns(UserWarning, restoration.denoise_bilateral, img) assert_warns(UserWarning, restoration.denoise_nl_means, img) def test_cycle_spinning_multichannel(): sigma = 0.1 rstate = np.random.RandomState(1234) for multichannel in True, False: if multichannel: img = astro # can either omit or be 0 along the channels axis valid_shifts = [1, (0, 1), (1, 0), (1, 1), (1, 1, 0)] # can either omit or be 1 on channels axis. valid_steps = [1, 2, (1, 2), (1, 2, 1)] # too few or too many shifts or non-zero shift on channels invalid_shifts = [(1, 1, 2), (1, ), (1, 1, 0, 1)] # too few or too many shifts or any shifts <= 0 invalid_steps = [(1, ), (1, 1, 1, 1), (0, 1), (-1, -1)] else: img = astro_gray valid_shifts = [1, (0, 1), (1, 0), (1, 1)] valid_steps = [1, 2, (1, 2)] invalid_shifts = [(1, 1, 2), (1, )] invalid_steps = [(1, ), (1, 1, 1), (0, 1), (-1, -1)] noisy = img.copy() + 0.1 * rstate.randn(*(img.shape)) denoise_func = restoration.denoise_wavelet func_kw = dict(sigma=sigma, multichannel=multichannel) # max_shifts=0 is equivalent to just calling denoise_func with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=0, func_kw=func_kw, multichannel=multichannel) dn = denoise_func(noisy, **func_kw) assert_equal(dn, dn_cc) # denoising with cycle spinning will give better PSNR than without for max_shifts in valid_shifts: with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=max_shifts, func_kw=func_kw, multichannel=multichannel) assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn)) for shift_steps in valid_steps: with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=2, shift_steps=shift_steps, func_kw=func_kw, multichannel=multichannel) assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn)) for max_shifts in invalid_shifts: with testing.raises(ValueError): dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=max_shifts, func_kw=func_kw, multichannel=multichannel) for shift_steps in invalid_steps: with testing.raises(ValueError): dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=2, shift_steps=shift_steps, func_kw=func_kw, multichannel=multichannel) def test_cycle_spinning_num_workers(): img = astro_gray sigma = 0.1 rstate = np.random.RandomState(1234) noisy = img.copy() + 0.1 * rstate.randn(*(img.shape)) denoise_func = restoration.denoise_wavelet func_kw = dict(sigma=sigma, multichannel=True) with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]): # same result whether using 1 worker or multiple workers dn_cc1 = restoration.cycle_spin(noisy, denoise_func, max_shifts=1, func_kw=func_kw, multichannel=False, num_workers=1) dn_cc2 = restoration.cycle_spin(noisy, denoise_func, max_shifts=1, func_kw=func_kw, multichannel=False, num_workers=4) dn_cc3 = restoration.cycle_spin(noisy, denoise_func, max_shifts=1, func_kw=func_kw, multichannel=False, num_workers=None) assert_almost_equal(dn_cc1, dn_cc2) assert_almost_equal(dn_cc1, dn_cc3) if __name__ == "__main__": testing.run_module_suite()