1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
|
# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Restoration computation module
------------------------------
This module provides image restoration techniques, such as
denoising, inpainting, and deblurring. These methods aim to recover
the original quality of images by removing artifacts, noise, or
distortions.
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# Note:
# ----
# - All `guidata.dataset.DataSet` parameter classes must also be imported
# in the `sigima.params` module.
# - All functions decorated by `computation_function` must be imported in the upper
# level `sigima.proc.image` module.
from __future__ import annotations
from typing import TYPE_CHECKING
import guidata.dataset as gds
import numpy as np
import pywt
from skimage import morphology, restoration
from sigima.config import _
from sigima.enums import ShrinkageMethod, ThresholdMethod, WaveletMode
from sigima.objects.image import ImageObj, ROI2DParam
from sigima.proc.decorator import computation_function
from sigima.proc.image.base import Wrap1to1Func, dst_1_to_1, restore_data_outside_roi
if TYPE_CHECKING:
import sigima.params
# NOTE: Only parameter classes DEFINED in this module should be included in __all__.
# Parameter classes imported from other modules (like sigima.proc.base) should NOT
# be re-exported to avoid Sphinx cross-reference conflicts. The sigima.params module
# serves as the central API point that imports and re-exports all parameter classes.
__all__ = [
"DenoiseBilateralParam",
"DenoiseTVParam",
"DenoiseWaveletParam",
"denoise_bilateral",
"denoise_tophat",
"denoise_tv",
"denoise_wavelet",
"erase",
]
class DenoiseTVParam(gds.DataSet):
"""Total Variation denoising parameters"""
weight = gds.FloatItem(
_("Denoising weight"),
default=0.1,
min=0,
nonzero=True,
help=_(
"The greater weight, the more denoising "
"(at the expense of fidelity to input)."
),
)
eps = gds.FloatItem(
"Epsilon",
default=0.0002,
min=0,
nonzero=True,
help=_(
"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"
),
)
max_num_iter = gds.IntItem(
_("Max. iterations"),
default=200,
min=0,
nonzero=True,
help=_("Maximal number of iterations used for the optimization"),
)
@computation_function()
def denoise_tv(src: ImageObj, p: DenoiseTVParam) -> ImageObj:
"""Compute Total Variation denoising
with :py:func:`skimage.restoration.denoise_tv_chambolle`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap1to1Func(
restoration.denoise_tv_chambolle,
weight=p.weight,
eps=p.eps,
max_num_iter=p.max_num_iter,
func_name="denoise_tv",
)(src)
class DenoiseBilateralParam(gds.DataSet):
"""Bilateral filter denoising parameters"""
sigma_spatial = gds.FloatItem(
"σ<sub>spatial</sub>",
default=1.0,
min=0,
nonzero=True,
unit="pixels",
help=_(
"Standard deviation for range distance. "
"A larger value results in averaging of pixels "
"with larger spatial differences."
),
)
mode = gds.ChoiceItem(_("Mode"), WaveletMode, default=WaveletMode.CONSTANT)
cval = gds.FloatItem(
"cval",
default=0.0,
help=_(
"Used in conjunction with mode 'constant', "
"the value outside the image boundaries."
),
)
@computation_function()
def denoise_bilateral(src: ImageObj, p: DenoiseBilateralParam) -> ImageObj:
"""Compute bilateral filter denoising
with :py:func:`skimage.restoration.denoise_bilateral`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap1to1Func(
restoration.denoise_bilateral,
sigma_spatial=p.sigma_spatial,
mode=p.mode,
cval=p.cval,
)(src)
class DenoiseWaveletParam(gds.DataSet):
"""Wavelet denoising parameters"""
wavelets = pywt.wavelist()
wavelet = gds.ChoiceItem(
_("Wavelet"), list(zip(wavelets, wavelets)), default="sym9"
)
mode = gds.ChoiceItem(_("Mode"), ThresholdMethod, default=ThresholdMethod.SOFT)
method = gds.ChoiceItem(
_("Method"), ShrinkageMethod, default=ShrinkageMethod.VISU_SHRINK
)
@computation_function()
def denoise_wavelet(src: ImageObj, p: DenoiseWaveletParam) -> ImageObj:
"""Compute Wavelet denoising
with :py:func:`skimage.restoration.denoise_wavelet`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap1to1Func(
restoration.denoise_wavelet, wavelet=p.wavelet, mode=p.mode, method=p.method
)(src)
@computation_function()
def denoise_tophat(src: ImageObj, p: sigima.params.MorphologyParam) -> ImageObj:
"""Denoise using White Top-Hat
with :py:func:`skimage.morphology.white_tophat`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_1_to_1(src, "denoise_tophat", f"radius={p.radius}")
dst.data = src.data - morphology.white_tophat(src.data, morphology.disk(p.radius))
restore_data_outside_roi(dst, src)
return dst
@computation_function()
def erase(src: ImageObj, p: ROI2DParam | list[ROI2DParam]) -> ImageObj:
"""Erase an area of the image using the mean value of the image.
.. note::
The erased area is defined by a region of interest (ROI) parameter set.
This ROI must not be mistaken with the ROI of the image object. If the
image object has a ROI, it is not used in this processing, except to
restore the data outside the ROI (as in all other processing).
Args:
src: input image object
p: parameters defining the area to erase (region of interest)
Returns:
Output image object
"""
params = [p] if isinstance(p, ROI2DParam) else p
suffix = None
if len(params) == 1:
suffix = params[0].get_suffix()
dst = dst_1_to_1(src, "erase", suffix)
for param in params:
value = np.nanmean(param.get_data(src))
erase_roi = param.to_single_roi(src)
mask = erase_roi.to_mask(src)
dst.data[~mask] = value
restore_data_outside_roi(dst, src)
return dst
|
