掩蔽归一化互相关

在这个例子中，我们使用掩码归一化互相关来识别两张包含无效数据的相似图像之间的相对偏移。

在这种情况下，图像不能在计算互相关之前简单地进行掩码处理，因为掩码会影响计算。必须从互相关中去除掩码的影响，如[1]_中所述。

在这个例子中，我们注册了两张图像之间的翻译。然而，其中一张图像约有25%的像素是损坏的。

[1]

D. Padfield, “Masked object registration in the Fourier domain” IEEE Transactions on Image Processing (2012). DOI:10.1109/TIP.2011.2181402

import numpy as np
import matplotlib.pyplot as plt

from skimage import data, draw
from skimage.registration import phase_cross_correlation
from scipy import ndimage as ndi

定义图像中无效的区域。无效像素的概率为25%。这可能是由于探测器故障，或不受平移影响的边缘（例如，窗口中的移动物体）。更多示例请参见参考论文。

image = data.camera()
shift = (-22, 13)

rng = np.random.default_rng()
corrupted_pixels = rng.choice([False, True], size=image.shape, p=[0.25, 0.75])

# The shift corresponds to the pixel offset relative to the reference image
offset_image = ndi.shift(image, shift)
offset_image *= corrupted_pixels
print(f'Known offset (row, col): {shift}')

# Determine what the mask is based on which pixels are invalid
# In this case, we know what the mask should be since we corrupted
# the pixels ourselves
mask = corrupted_pixels

detected_shift, _, _ = phase_cross_correlation(image, offset_image, reference_mask=mask)

print(f'Detected pixel offset (row, col): {-detected_shift}')

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(8, 3))

ax1.imshow(image, cmap='gray')
ax1.set_axis_off()
ax1.set_title('Reference image')

ax2.imshow(offset_image.real, cmap='gray')
ax2.set_axis_off()
ax2.set_title('Corrupted, offset image')

ax3.imshow(mask, cmap='gray')
ax3.set_axis_off()
ax3.set_title('Masked pixels')


plt.show()

Known offset (row, col): (-22, 13)
Detected pixel offset (row, col): [-22.  13.]

固体掩膜是另一个说明性的例子。在这种情况下，我们对图像有一个有限的视角，并且有一个偏移图像。这些图像的掩膜不需要相同。phase_cross_correlation 函数将正确识别图像的哪一部分应该进行比较。

image = data.camera()
shift = (-22, 13)

rr1, cc1 = draw.ellipse(259, 248, r_radius=125, c_radius=100, shape=image.shape)

rr2, cc2 = draw.ellipse(300, 200, r_radius=110, c_radius=180, shape=image.shape)

mask1 = np.zeros_like(image, dtype=bool)
mask2 = np.zeros_like(image, dtype=bool)
mask1[rr1, cc1] = True
mask2[rr2, cc2] = True

offset_image = ndi.shift(image, shift)
image *= mask1
offset_image *= mask2

print(f'Known offset (row, col): {shift}')

detected_shift, _, _ = phase_cross_correlation(
    image, offset_image, reference_mask=mask1, moving_mask=mask2
)

print(f'Detected pixel offset (row, col): {-detected_shift}')

fig = plt.figure(figsize=(8, 3))
ax1 = plt.subplot(1, 2, 1)
ax2 = plt.subplot(1, 2, 2, sharex=ax1, sharey=ax1)

ax1.imshow(image, cmap='gray')
ax1.set_axis_off()
ax1.set_title('Reference image')

ax2.imshow(offset_image.real, cmap='gray')
ax2.set_axis_off()
ax2.set_title('Masked, offset image')

plt.show()

Known offset (row, col): (-22, 13)
Detected pixel offset (row, col): [-22.  13.]