掩蔽归一化互相关

在本例中，我们使用掩蔽归一化互相关来识别包含无效数据的两幅相似图像之间的相对偏移。

在这种情况下，在计算互相关之前不能简单地对图像进行掩蔽，因为掩蔽会影响计算。必须从互相关中去除掩蔽的影响，如[1]中所述。

在本例中，我们配准了两幅图像之间的平移。但是，其中一幅图像大约有 25% 的像素已损坏。

[1]

D. Padfield，“傅里叶域中的掩蔽对象配准” IEEE 图像处理交易 (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%。这可能是由于检测器故障或不受平移影响的边缘（例如，窗口中的移动物体）造成的。有关更多示例，请参阅参考论文

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.]

实心掩码是另一个说明性示例。在这种情况下，我们对图像和偏移图像的视野有限。这些图像的掩码不必相同。phase_cross_correlation 函数将正确识别应比较图像的哪一部分。

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.]