图像反卷积#

在本示例中，我们使用 Richardson-Lucy 反卷积算法对图像进行反卷积 ([1], [2])。

该算法基于 PSF（点扩散函数），其中 PSF 被描述为光学系统的冲激响应。通过多次迭代对模糊图像进行锐化，需要手动调整迭代次数。

Original Data, Noisy data, Restoration using Richardson-Lucy

import numpy as np
import matplotlib.pyplot as plt

from scipy.signal import convolve2d as conv2

from skimage import color, data, restoration

rng = np.random.default_rng()

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 += (rng.poisson(lam=25, size=astro.shape) - 10) / 255.0

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

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 5))
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()

脚本的总运行时间：（0 分钟 0.588 秒）

由 Sphinx-Gallery 生成的画廊