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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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import scipy.stats
import numpy as np
from math import ceil
from .. import img_as_float
from ._denoise_cy import _denoise_bilateral, _denoise_tv_bregman
from .._shared.utils import warn
import pywt
import skimage.color as color
from skimage.color.colorconv import ycbcr_from_rgb
import numbers
def _gaussian_weight(array, sigma_squared, *, dtype=float):
"""Helping function. Define a Gaussian weighting from array and
sigma_square.
Parameters
----------
array : ndarray
Input array.
sigma_squared : float
The squared standard deviation used in the filter.
dtype : data type object, optional (default : float)
The type and size of the data to be returned.
Returns
-------
gaussian : ndarray
The input array filtered by the Gaussian.
"""
return np.exp(-0.5 * (array ** 2 / sigma_squared), dtype=dtype)
def _compute_color_lut(bins, sigma, max_value, *, dtype=float):
"""Helping function. Define a lookup table containing Gaussian filter
values using the color distance sigma.
Parameters
----------
bins : int
Number of discrete values for Gaussian weights of color filtering.
A larger value results in improved accuracy.
sigma : 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.
max_value : float
Maximum value of the input image.
dtype : data type object, optional (default : float)
The type and size of the data to be returned.
Returns
-------
color_lut : ndarray
Lookup table for the color distance sigma.
"""
values = np.linspace(0, max_value, bins, endpoint=False)
return _gaussian_weight(values, sigma**2, dtype=dtype)
def _compute_spatial_lut(win_size, sigma, *, dtype=float):
"""Helping function. Define a lookup table containing Gaussian filter
values using the spatial sigma.
Parameters
----------
win_size : int
Window size for filtering.
If win_size is not specified, it is calculated as
``max(5, 2 * ceil(3 * sigma_spatial) + 1)``.
sigma : float
Standard deviation for range distance. A larger value results in
averaging of pixels with larger spatial differences.
dtype : data type object
The type and size of the data to be returned.
Returns
-------
spatial_lut : ndarray
Lookup table for the spatial sigma.
"""
grid_points = np.arange(-win_size // 2, win_size // 2 + 1)
rr, cc = np.meshgrid(grid_points, grid_points, indexing='ij')
distances = np.hypot(rr, cc)
return _gaussian_weight(distances, sigma**2, dtype=dtype).ravel()
def denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1,
bins=10000, mode='constant', cval=0, multichannel=False):
"""Denoise image using bilateral filter.
Parameters
----------
image : ndarray, shape (M, N[, 3])
Input image, 2D grayscale or RGB.
win_size : int
Window size for filtering.
If win_size is not specified, it is calculated as
``max(5, 2 * ceil(3 * sigma_spatial) + 1)``.
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.
mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}
How to handle values outside the image borders. See
`numpy.pad` for detail.
cval : string
Used in conjunction with mode 'constant', the value outside
the image boundaries.
multichannel : bool
Whether the last axis of the image is to be interpreted as multiple
channels or another spatial dimension.
Returns
-------
denoised : ndarray
Denoised image.
Notes
-----
This is an edge-preserving, denoising filter. It averages pixels based on
their spatial closeness and radiometric similarity [1]_.
Spatial closeness is measured by the Gaussian function of the Euclidean
distance between two pixels and a certain standard deviation
(`sigma_spatial`).
Radiometric similarity is measured by the Gaussian function of the
Euclidean distance between two color values and a certain standard
deviation (`sigma_color`).
References
----------
.. [1] C. Tomasi and R. Manduchi. "Bilateral Filtering for Gray and Color
Images." IEEE International Conference on Computer Vision (1998)
839-846. :DOI:`10.1109/ICCV.1998.710815`
Examples
--------
>>> from skimage import data, img_as_float
>>> astro = img_as_float(data.astronaut())
>>> astro = astro[220:300, 220:320]
>>> noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape)
>>> noisy = np.clip(noisy, 0, 1)
>>> denoised = denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15,
... multichannel=True)
"""
if multichannel:
if image.ndim != 3:
if image.ndim == 2:
raise ValueError("Use ``multichannel=False`` for 2D grayscale "
"images. The last axis of the input image "
"must be multiple color channels not another "
"spatial dimension.")
else:
raise ValueError("Bilateral filter is only implemented for "
"2D grayscale images (image.ndim == 2) and "
"2D multichannel (image.ndim == 3) images, "
"but the input image has {0} dimensions. "
"".format(image.ndim))
elif image.shape[2] not in (3, 4):
if image.shape[2] > 4:
msg = ("The last axis of the input image is interpreted as "
"channels. Input image with shape {0} has {1} channels "
"in last axis. ``denoise_bilateral`` is implemented "
"for 2D grayscale and color images only")
warn(msg.format(image.shape, image.shape[2]))
else:
msg = "Input image must be grayscale, RGB, or RGBA; " \
"but has shape {0}."
warn(msg.format(image.shape))
else:
if image.ndim > 2:
raise ValueError("Bilateral filter is not implemented for "
"grayscale images of 3 or more dimensions, "
"but input image has {0} dimension. Use "
"``multichannel=True`` for 2-D RGB "
"images.".format(image.shape))
if win_size is None:
win_size = max(5, 2 * int(ceil(3 * sigma_spatial)) + 1)
min_value = image.min()
max_value = image.max()
if min_value == max_value:
return image
# if image.max() is 0, then dist_scale can have an unverified value
# and color_lut[<int>(dist * dist_scale)] may cause a segmentation fault
# so we verify we have a positive image and that the max is not 0.0.
if min_value < 0.0:
raise ValueError("Image must contain only positive values")
if max_value == 0.0:
raise ValueError("The maximum value found in the image was 0.")
image = np.atleast_3d(img_as_float(image))
image = np.ascontiguousarray(image)
sigma_color = sigma_color or image.std()
color_lut = _compute_color_lut(bins, sigma_color, max_value,
dtype=image.dtype)
range_lut = _compute_spatial_lut(win_size, sigma_spatial, dtype=image.dtype)
out = np.empty(image.shape, dtype=image.dtype)
dims = image.shape[2]
# There are a number of arrays needed in the Cython function.
# It's easier to allocate them outside of Cython so that all
# arrays are in the same type, then just copy the empty array
# where needed within Cython.
empty_dims = np.empty(dims, dtype=image.dtype)
return _denoise_bilateral(image, image.max(), win_size, sigma_color,
sigma_spatial, bins, mode, cval, color_lut,
range_lut, empty_dims, out)
def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True,
*, multichannel=False):
"""Perform total-variation denoising using split-Bregman optimization.
Total-variation denoising (also know as total-variation regularization)
tries to find an image with less total-variation under the constraint
of being similar to the input image, which is controlled by the
regularization parameter ([1]_, [2]_, [3]_, [4]_).
Parameters
----------
image : ndarray
Input data to be denoised (converted using img_as_float`).
weight : float
Denoising weight. The smaller the `weight`, the more denoising (at
the expense of less similarity to the `input`). The regularization
parameter `lambda` is chosen as `2 * weight`.
eps : float, optional
Relative difference of the value of the cost function that determines
the stop criterion. The algorithm stops when::
SUM((u(n) - u(n-1))**2) < eps
max_iter : int, optional
Maximal number of iterations used for the optimization.
isotropic : boolean, optional
Switch between isotropic and anisotropic TV denoising.
multichannel : bool, optional
Apply total-variation denoising separately for each channel. This
option should be true for color images, otherwise the denoising is
also applied in the channels dimension.
Returns
-------
u : ndarray
Denoised image.
References
----------
.. [1] https://en.wikipedia.org/wiki/Total_variation_denoising
.. [2] Tom Goldstein and Stanley Osher, "The Split Bregman Method For L1
Regularized Problems",
ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf
.. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising
using Split Bregman" in Image Processing On Line on 2012–05–19,
https://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf
.. [4] https://web.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf
"""
image = np.atleast_3d(img_as_float(image))
rows = image.shape[0]
cols = image.shape[1]
dims = image.shape[2]
shape_ext = (rows + 2, cols + 2, dims)
out = np.zeros(shape_ext, image.dtype)
if multichannel:
channel_out = np.zeros(shape_ext[:2] + (1,), dtype=out.dtype)
for c in range(image.shape[-1]):
# the algorithm below expects 3 dimensions to always be present.
# slicing the array in this fashion preserves the channel dimension for us
channel_in = np.ascontiguousarray(image[..., c:c+1])
_denoise_tv_bregman(channel_in, image.dtype.type(weight),
max_iter, eps, isotropic, channel_out)
out[..., c] = channel_out[..., 0]
else:
image = np.ascontiguousarray(image)
_denoise_tv_bregman(image, image.dtype.type(weight), max_iter, eps,
isotropic, out)
return np.squeeze(out[1:-1, 1:-1])
def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.e-4, n_iter_max=200):
"""Perform total-variation denoising on n-dimensional images.
Parameters
----------
image : ndarray
n-D input data to be denoised.
weight : float, optional
Denoising weight. The greater `weight`, the more denoising (at
the expense of fidelity to `input`).
eps : float, optional
Relative difference of the value of the cost function that determines
the stop criterion. The algorithm stops when:
(E_(n-1) - E_n) < eps * E_0
n_iter_max : int, optional
Maximal number of iterations used for the optimization.
Returns
-------
out : ndarray
Denoised array of floats.
Notes
-----
Rudin, Osher and Fatemi algorithm.
"""
ndim = image.ndim
p = np.zeros((image.ndim, ) + image.shape, dtype=image.dtype)
g = np.zeros_like(p)
d = np.zeros_like(image)
i = 0
while i < n_iter_max:
if i > 0:
# d will be the (negative) divergence of p
d = -p.sum(0)
slices_d = [slice(None), ] * ndim
slices_p = [slice(None), ] * (ndim + 1)
for ax in range(ndim):
slices_d[ax] = slice(1, None)
slices_p[ax+1] = slice(0, -1)
slices_p[0] = ax
d[tuple(slices_d)] += p[tuple(slices_p)]
slices_d[ax] = slice(None)
slices_p[ax+1] = slice(None)
out = image + d
else:
out = image
E = (d ** 2).sum()
# g stores the gradients of out along each axis
# e.g. g[0] is the first order finite difference along axis 0
slices_g = [slice(None), ] * (ndim + 1)
for ax in range(ndim):
slices_g[ax+1] = slice(0, -1)
slices_g[0] = ax
g[tuple(slices_g)] = np.diff(out, axis=ax)
slices_g[ax+1] = slice(None)
norm = np.sqrt((g ** 2).sum(axis=0))[np.newaxis, ...]
E += weight * norm.sum()
tau = 1. / (2.*ndim)
norm *= tau / weight
norm += 1.
p -= tau * g
p /= norm
E /= float(image.size)
if i == 0:
E_init = E
E_previous = E
else:
if np.abs(E_previous - E) < eps * E_init:
break
else:
E_previous = E
i += 1
return out
def denoise_tv_chambolle(image, weight=0.1, eps=2.e-4, n_iter_max=200,
multichannel=False):
"""Perform total-variation denoising on n-dimensional images.
Parameters
----------
image : ndarray of ints, uints or floats
Input data to be denoised. `image` can be of any numeric type,
but it is cast into an ndarray of floats for the computation
of the denoised image.
weight : float, optional
Denoising weight. The greater `weight`, the more denoising (at
the expense of fidelity to `input`).
eps : float, optional
Relative difference of the value of the cost function that
determines the stop criterion. The algorithm stops when:
(E_(n-1) - E_n) < eps * E_0
n_iter_max : int, optional
Maximal number of iterations used for the optimization.
multichannel : bool, optional
Apply total-variation denoising separately for each channel. This
option should be true for color images, otherwise the denoising is
also applied in the channels dimension.
Returns
-------
out : ndarray
Denoised image.
Notes
-----
Make sure to set the multichannel parameter appropriately for color images.
The principle of total variation denoising is explained in
https://en.wikipedia.org/wiki/Total_variation_denoising
The principle of total variation denoising is to minimize the
total variation of the image, which can be roughly described as
the integral of the norm of the image gradient. Total variation
denoising tends to produce "cartoon-like" images, that is,
piecewise-constant images.
This code is an implementation of the algorithm of Rudin, Fatemi and Osher
that was proposed by Chambolle in [1]_.
References
----------
.. [1] A. Chambolle, An algorithm for total variation minimization and
applications, Journal of Mathematical Imaging and Vision,
Springer, 2004, 20, 89-97.
Examples
--------
2D example on astronaut image:
>>> from skimage import color, data
>>> img = color.rgb2gray(data.astronaut())[:50, :50]
>>> img += 0.5 * img.std() * np.random.randn(*img.shape)
>>> denoised_img = denoise_tv_chambolle(img, weight=60)
3D example on synthetic data:
>>> x, y, z = np.ogrid[0:20, 0:20, 0:20]
>>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
>>> mask = mask.astype(np.float)
>>> mask += 0.2*np.random.randn(*mask.shape)
>>> res = denoise_tv_chambolle(mask, weight=100)
"""
im_type = image.dtype
if not im_type.kind == 'f':
image = img_as_float(image)
if multichannel:
out = np.zeros_like(image)
for c in range(image.shape[-1]):
out[..., c] = _denoise_tv_chambolle_nd(image[..., c], weight, eps,
n_iter_max)
else:
out = _denoise_tv_chambolle_nd(image, weight, eps, n_iter_max)
return out
def _bayes_thresh(details, var):
"""BayesShrink threshold for a zero-mean details coeff array."""
# Equivalent to: dvar = np.var(details) for 0-mean details array
dvar = np.mean(details*details)
eps = np.finfo(details.dtype).eps
thresh = var / np.sqrt(max(dvar - var, eps))
return thresh
def _universal_thresh(img, sigma):
""" Universal threshold used by the VisuShrink method """
return sigma*np.sqrt(2*np.log(img.size))
def _sigma_est_dwt(detail_coeffs, distribution='Gaussian'):
"""Calculate the robust median estimator of the noise standard deviation.
Parameters
----------
detail_coeffs : ndarray
The detail coefficients corresponding to the discrete wavelet
transform of an image.
distribution : str
The underlying noise distribution.
Returns
-------
sigma : float
The estimated noise standard deviation (see section 4.2 of [1]_).
References
----------
.. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
:DOI:`10.1093/biomet/81.3.425`
"""
# Consider regions with detail coefficients exactly zero to be masked out
detail_coeffs = detail_coeffs[np.nonzero(detail_coeffs)]
if distribution.lower() == 'gaussian':
# 75th quantile of the underlying, symmetric noise distribution
denom = scipy.stats.norm.ppf(0.75)
sigma = np.median(np.abs(detail_coeffs)) / denom
else:
raise ValueError("Only Gaussian noise estimation is currently "
"supported")
return sigma
def _wavelet_threshold(image, wavelet, method=None, threshold=None,
sigma=None, mode='soft', wavelet_levels=None):
"""Perform wavelet thresholding.
Parameters
----------
image : ndarray (2d or 3d) of ints, uints or floats
Input data to be denoised. `image` can be of any numeric type,
but it is cast into an ndarray of floats for the computation
of the denoised image.
wavelet : string
The type of wavelet to perform. Can be any of the options
pywt.wavelist outputs. For example, this may be any of ``{db1, db2,
db3, db4, haar}``.
method : {'BayesShrink', 'VisuShrink'}, optional
Thresholding method to be used. The currently supported methods are
"BayesShrink" [1]_ and "VisuShrink" [2]_. If it is set to None, a
user-specified ``threshold`` must be supplied instead.
threshold : float, optional
The thresholding value to apply during wavelet coefficient
thresholding. The default value (None) uses the selected ``method`` to
estimate appropriate threshold(s) for noise removal.
sigma : float, optional
The standard deviation of the noise. The noise is estimated when sigma
is None (the default) by the method in [2]_.
mode : {'soft', 'hard'}, optional
An optional argument to choose the type of denoising performed. It
noted that choosing soft thresholding given additive noise finds the
best approximation of the original image.
wavelet_levels : int or None, optional
The number of wavelet decomposition levels to use. The default is
three less than the maximum number of possible decomposition levels
(see Notes below).
Returns
-------
out : ndarray
Denoised image.
References
----------
.. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet
thresholding for image denoising and compression." Image Processing,
IEEE Transactions on 9.9 (2000): 1532-1546.
:DOI:`10.1109/83.862633`
.. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
:DOI:`10.1093/biomet/81.3.425`
"""
wavelet = pywt.Wavelet(wavelet)
if not wavelet.orthogonal:
warn(("Wavelet thresholding was designed for use with orthogonal "
"wavelets. For nonorthogonal wavelets such as {}, results are "
"likely to be suboptimal.").format(wavelet.name))
# original_extent is used to workaround PyWavelets issue #80
# odd-sized input results in an image with 1 extra sample after waverecn
original_extent = tuple(slice(s) for s in image.shape)
# Determine the number of wavelet decomposition levels
if wavelet_levels is None:
# Determine the maximum number of possible levels for image
dlen = wavelet.dec_len
wavelet_levels = pywt.dwtn_max_level(image.shape, wavelet)
# Skip coarsest wavelet scales (see Notes in docstring).
wavelet_levels = max(wavelet_levels - 3, 1)
coeffs = pywt.wavedecn(image, wavelet=wavelet, level=wavelet_levels)
# Detail coefficients at each decomposition level
dcoeffs = coeffs[1:]
if sigma is None:
# Estimate the noise via the method in [2]_
detail_coeffs = dcoeffs[-1]['d' * image.ndim]
sigma = _sigma_est_dwt(detail_coeffs, distribution='Gaussian')
if method is not None and threshold is not None:
warn(("Thresholding method {} selected. The user-specified threshold "
"will be ignored.").format(method))
if threshold is None:
var = sigma**2
if method is None:
raise ValueError(
"If method is None, a threshold must be provided.")
elif method == "BayesShrink":
# The BayesShrink thresholds from [1]_ in docstring
threshold = [{key: _bayes_thresh(level[key], var) for key in level}
for level in dcoeffs]
elif method == "VisuShrink":
# The VisuShrink thresholds from [2]_ in docstring
threshold = _universal_thresh(image, sigma)
else:
raise ValueError("Unrecognized method: {}".format(method))
if np.isscalar(threshold):
# A single threshold for all coefficient arrays
denoised_detail = [{key: pywt.threshold(level[key],
value=threshold,
mode=mode) for key in level}
for level in dcoeffs]
else:
# Dict of unique threshold coefficients for each detail coeff. array
denoised_detail = [{key: pywt.threshold(level[key],
value=thresh[key],
mode=mode) for key in level}
for thresh, level in zip(threshold, dcoeffs)]
denoised_coeffs = [coeffs[0]] + denoised_detail
return pywt.waverecn(denoised_coeffs, wavelet)[original_extent]
def _scale_sigma_and_image_consistently(image, sigma, multichannel,
rescale_sigma):
"""If the ``image`` is rescaled, also rescale ``sigma`` consistently.
Images that are not floating point will be rescaled via ``img_as_float``.
"""
if multichannel:
if isinstance(sigma, numbers.Number) or sigma is None:
sigma = [sigma] * image.shape[-1]
elif len(sigma) != image.shape[-1]:
raise ValueError(
"When multichannel is True, sigma must be a scalar or have "
"length equal to the number of channels")
if image.dtype.kind != 'f':
if rescale_sigma:
range_pre = image.max() - image.min()
image = img_as_float(image)
if rescale_sigma:
range_post = image.max() - image.min()
# apply the same magnitude scaling to sigma
scale_factor = range_post / range_pre
if multichannel:
sigma = [s * scale_factor if s is not None else s
for s in sigma]
elif sigma is not None:
sigma *= scale_factor
return image, sigma
def _rescale_sigma_rgb2ycbcr(sigmas):
"""Convert user-provided noise standard deviations to YCbCr space.
Notes
-----
If R, G, B are linearly independent random variables and a1, a2, a3 are
scalars, then random variable C:
C = a1 * R + a2 * G + a3 * B
has variance, var_C, given by:
var_C = a1**2 * var_R + a2**2 * var_G + a3**2 * var_B
"""
if sigmas[0] is None:
return sigmas
sigmas = np.asarray(sigmas)
rgv_variances = sigmas * sigmas
for i in range(3):
scalars = ycbcr_from_rgb[i, :]
var_channel = np.sum(scalars * scalars * rgv_variances)
sigmas[i] = np.sqrt(var_channel)
return sigmas
def denoise_wavelet(image, sigma=None, wavelet='db1', mode='soft',
wavelet_levels=None, multichannel=False,
convert2ycbcr=False, method='BayesShrink',
rescale_sigma=None):
"""Perform wavelet denoising on an image.
Parameters
----------
image : ndarray ([M[, N[, ...P]][, C]) of ints, uints or floats
Input data to be denoised. `image` can be of any numeric type,
but it is cast into an ndarray of floats for the computation
of the denoised image.
sigma : float or list, optional
The noise standard deviation used when computing the wavelet detail
coefficient threshold(s). When None (default), the noise standard
deviation is estimated via the method in [2]_.
wavelet : string, optional
The type of wavelet to perform and can be any of the options
``pywt.wavelist`` outputs. The default is `'db1'`. For example,
``wavelet`` can be any of ``{'db2', 'haar', 'sym9'}`` and many more.
mode : {'soft', 'hard'}, optional
An optional argument to choose the type of denoising performed. It
noted that choosing soft thresholding given additive noise finds the
best approximation of the original image.
wavelet_levels : int or None, optional
The number of wavelet decomposition levels to use. The default is
three less than the maximum number of possible decomposition levels.
multichannel : bool, optional
Apply wavelet denoising separately for each channel (where channels
correspond to the final axis of the array).
convert2ycbcr : bool, optional
If True and multichannel True, do the wavelet denoising in the YCbCr
colorspace instead of the RGB color space. This typically results in
better performance for RGB images.
method : {'BayesShrink', 'VisuShrink'}, optional
Thresholding method to be used. The currently supported methods are
"BayesShrink" [1]_ and "VisuShrink" [2]_. Defaults to "BayesShrink".
rescale_sigma : bool or None, optional
If False, no rescaling of the user-provided ``sigma`` will be
performed. The default of ``None`` rescales sigma appropriately if the
image is rescaled internally. A ``DeprecationWarning`` is raised to
warn the user about this new behaviour. This warning can be avoided
by setting ``rescale_sigma=True``.
.. versionadded:: 0.16
``rescale_sigma`` was introduced in 0.16
Returns
-------
out : ndarray
Denoised image.
Notes
-----
The wavelet domain is a sparse representation of the image, and can be
thought of similarly to the frequency domain of the Fourier transform.
Sparse representations have most values zero or near-zero and truly random
noise is (usually) represented by many small values in the wavelet domain.
Setting all values below some threshold to 0 reduces the noise in the
image, but larger thresholds also decrease the detail present in the image.
If the input is 3D, this function performs wavelet denoising on each color
plane separately.
.. versionchanged:: 0.16
For floating point inputs, the original input range is maintained and
there is no clipping applied to the output. Other input types will be
converted to a floating point value in the range [-1, 1] or [0, 1]
depending on the input image range. Unless ``rescale_sigma = False``,
any internal rescaling applied to the ``image`` will also be applied
to ``sigma`` to maintain the same relative amplitude.
Many wavelet coefficient thresholding approaches have been proposed. By
default, ``denoise_wavelet`` applies BayesShrink, which is an adaptive
thresholding method that computes separate thresholds for each wavelet
sub-band as described in [1]_.
If ``method == "VisuShrink"``, a single "universal threshold" is applied to
all wavelet detail coefficients as described in [2]_. This threshold
is designed to remove all Gaussian noise at a given ``sigma`` with high
probability, but tends to produce images that appear overly smooth.
Although any of the wavelets from ``PyWavelets`` can be selected, the
thresholding methods assume an orthogonal wavelet transform and may not
choose the threshold appropriately for biorthogonal wavelets. Orthogonal
wavelets are desirable because white noise in the input remains white noise
in the subbands. Biorthogonal wavelets lead to colored noise in the
subbands. Additionally, the orthogonal wavelets in PyWavelets are
orthonormal so that noise variance in the subbands remains identical to the
noise variance of the input. Example orthogonal wavelets are the Daubechies
(e.g. 'db2') or symmlet (e.g. 'sym2') families.
References
----------
.. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet
thresholding for image denoising and compression." Image Processing,
IEEE Transactions on 9.9 (2000): 1532-1546.
:DOI:`10.1109/83.862633`
.. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
:DOI:`10.1093/biomet/81.3.425`
Examples
--------
>>> from skimage import color, data
>>> img = img_as_float(data.astronaut())
>>> img = color.rgb2gray(img)
>>> img += 0.1 * np.random.randn(*img.shape)
>>> img = np.clip(img, 0, 1)
>>> denoised_img = denoise_wavelet(img, sigma=0.1, rescale_sigma=True)
"""
if method not in ["BayesShrink", "VisuShrink"]:
raise ValueError(
('Invalid method: {}. The currently supported methods are '
'"BayesShrink" and "VisuShrink"').format(method))
# floating-point inputs are not rescaled, so don't clip their output.
clip_output = image.dtype.kind != 'f'
if convert2ycbcr and not multichannel:
raise ValueError("convert2ycbcr requires multichannel == True")
if rescale_sigma is None:
msg = (
"As of scikit-image 0.16, automated rescaling of sigma to match "
"any internal rescaling of the image is performed. Setting "
"rescale_sigma to False, will disable this new behaviour. To "
"avoid this warning the user should explicitly set rescale_sigma "
"to True or False."
)
warn(msg, FutureWarning, stacklevel=2)
rescale_sigma = True
image, sigma = _scale_sigma_and_image_consistently(image,
sigma,
multichannel,
rescale_sigma)
if multichannel:
if convert2ycbcr:
out = color.rgb2ycbcr(image)
# convert user-supplied sigmas to the new colorspace as well
if rescale_sigma:
sigma = _rescale_sigma_rgb2ycbcr(sigma)
for i in range(3):
# renormalizing this color channel to live in [0, 1]
_min, _max = out[..., i].min(), out[..., i].max()
scale_factor = _max - _min
if scale_factor == 0:
# skip any channel containing only zeros!
continue
channel = out[..., i] - _min
channel /= scale_factor
sigma_channel = sigma[i]
if sigma_channel is not None:
sigma_channel /= scale_factor
out[..., i] = denoise_wavelet(channel,
wavelet=wavelet,
method=method,
sigma=sigma_channel,
mode=mode,
wavelet_levels=wavelet_levels,
rescale_sigma=rescale_sigma)
out[..., i] = out[..., i] * scale_factor
out[..., i] += _min
out = color.ycbcr2rgb(out)
else:
out = np.empty_like(image)
for c in range(image.shape[-1]):
out[..., c] = _wavelet_threshold(image[..., c],
wavelet=wavelet,
method=method,
sigma=sigma[c], mode=mode,
wavelet_levels=wavelet_levels)
else:
out = _wavelet_threshold(image, wavelet=wavelet, method=method,
sigma=sigma, mode=mode,
wavelet_levels=wavelet_levels)
if clip_output:
clip_range = (-1, 1) if image.min() < 0 else (0, 1)
out = np.clip(out, *clip_range, out=out)
return out
def estimate_sigma(image, average_sigmas=False, multichannel=False):
"""
Robust wavelet-based estimator of the (Gaussian) noise standard deviation.
Parameters
----------
image : ndarray
Image for which to estimate the noise standard deviation.
average_sigmas : bool, optional
If true, average the channel estimates of `sigma`. Otherwise return
a list of sigmas corresponding to each channel.
multichannel : bool
Estimate sigma separately for each channel.
Returns
-------
sigma : float or list
Estimated noise standard deviation(s). If `multichannel` is True and
`average_sigmas` is False, a separate noise estimate for each channel
is returned. Otherwise, the average of the individual channel
estimates is returned.
Notes
-----
This function assumes the noise follows a Gaussian distribution. The
estimation algorithm is based on the median absolute deviation of the
wavelet detail coefficients as described in section 4.2 of [1]_.
References
----------
.. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
:DOI:`10.1093/biomet/81.3.425`
Examples
--------
>>> import skimage.data
>>> from skimage import img_as_float
>>> img = img_as_float(skimage.data.camera())
>>> sigma = 0.1
>>> img = img + sigma * np.random.standard_normal(img.shape)
>>> sigma_hat = estimate_sigma(img, multichannel=False)
"""
if multichannel:
nchannels = image.shape[-1]
sigmas = [estimate_sigma(
image[..., c], multichannel=False) for c in range(nchannels)]
if average_sigmas:
sigmas = np.mean(sigmas)
return sigmas
elif image.shape[-1] <= 4:
msg = ("image is size {0} on the last axis, but multichannel is "
"False. If this is a color image, please set multichannel "
"to True for proper noise estimation.")
warn(msg.format(image.shape[-1]))
coeffs = pywt.dwtn(image, wavelet='db2')
detail_coeffs = coeffs['d' * image.ndim]
return _sigma_est_dwt(detail_coeffs, distribution='Gaussian')