scikit-image库----图像反卷积修复（二十一）_图像修复lr算法

维纳过滤器（Wiener filter）

基于PSF（点扩散函数）的逆滤波器，先前的正则化（高频惩罚）以及数据与先前充分性之间的权衡。 必须手动调整正则化参数。
在此示例中，我们使用Wiener和无监督的Wiener算法对图像的噪声版本进行去卷积。 该算法基于线性模型，不能像非线性方法（如电视恢复）那样恢复锐边，但速度要快得多。

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from skimage import color, data, restoration

astro = color.rgb2gray(data.astronaut())
from scipy.signal import convolve2d as conv2
psf = np.ones((5, 5)) / 25
astro = conv2(astro, psf, 'same')
astro += 0.1 * astro.std() * np.random.standard_normal(astro.shape)

deconvolved, _ = restoration.unsupervised_wiener(astro, psf)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5),
                       sharex=True, sharey=True)

plt.gray()

ax[0].imshow(astro, vmin=deconvolved.min(), vmax=deconvolved.max())
ax[0].axis('off')
ax[0].set_title('Data')

ax[1].imshow(deconvolved)
ax[1].axis('off')
ax[1].set_title('Self tuned restoration')

fig.tight_layout()

plt.show()
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Lucy-Richardson算法

维纳滤波是频域的图像修复算法，而LR算法是时域的一种图像修复算法。该算法基于PSF（点扩散函数），PSF被描述为光学系统的脉冲响应。 通过多次迭代锐化模糊图像，需要手动调整

import numpy as np
import matplotlib.pyplot as plt

from scipy.signal import convolve2d as conv2

from skimage import color, data, restoration

astro = color.rgb2gray(data.astronaut())

psf = np.ones((5, 5)) / 25
astro = conv2(astro, psf, 'same')
# Add Noise to Image
astro_noisy = astro.copy()
astro_noisy += (np.random.poisson(lam=25, size=astro.shape) - 10) / 255.

# Restore Image using Richardson-Lucy algorithm
deconvolved_RL = restoration.richardson_lucy(astro_noisy, psf, iterations=30)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(18, 9))
plt.gray()

for a in (ax[0], ax[1], ax[2]):
       a.axis('off')

ax[0].imshow(astro)
ax[0].set_title('Original Data')

ax[1].imshow(astro_noisy)
ax[1].set_title('Noisy data')

ax[2].imshow(deconvolved_RL, vmin=astro_noisy.min(), vmax=astro_noisy.max())
ax[2].set_title('Restoration using\nRichardson-Lucy')


fig.subplots_adjust(wspace=0.02, hspace=0.2,
                    top=0.9, bottom=0.05, left=0, right=1)
plt.show()
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