高斯差分带通滤波

带通滤波器衰减目标范围（带）之外的信号频率。在图像分析中，它们可用于对图像进行降噪，同时减少低频伪像，例如不均匀照明。带通滤波器可用于查找图像特征，例如斑点和边缘。

将带通滤波器应用于图像的一种方法是从模糊度较低的图像中减去使用高斯核模糊的图像。此示例显示了高斯差分方法在带通滤波中的两种应用。

对图像进行降噪并减少阴影

import matplotlib.pyplot as plt
import numpy as np
from skimage.data import gravel
from skimage.filters import difference_of_gaussians, window
from scipy.fft import fftn, fftshift

image = gravel()
wimage = image * window('hann', image.shape)  # window image to improve FFT
filtered_image = difference_of_gaussians(image, 1, 12)
filtered_wimage = filtered_image * window('hann', image.shape)
im_f_mag = fftshift(np.abs(fftn(wimage)))
fim_f_mag = fftshift(np.abs(fftn(filtered_wimage)))

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
ax[0, 0].imshow(image, cmap='gray')
ax[0, 0].set_title('Original Image')
ax[0, 1].imshow(np.log(im_f_mag), cmap='magma')
ax[0, 1].set_title('Original FFT Magnitude (log)')
ax[1, 0].imshow(filtered_image, cmap='gray')
ax[1, 0].set_title('Filtered Image')
ax[1, 1].imshow(np.log(fim_f_mag), cmap='magma')
ax[1, 1].set_title('Filtered FFT Magnitude (log)')
plt.show()

增强图像中的边缘

from skimage.data import camera

image = camera()
wimage = image * window('hann', image.shape)  # window image to improve FFT
filtered_image = difference_of_gaussians(image, 1.5)
filtered_wimage = filtered_image * window('hann', image.shape)
im_f_mag = fftshift(np.abs(fftn(wimage)))
fim_f_mag = fftshift(np.abs(fftn(filtered_wimage)))

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
ax[0, 0].imshow(image, cmap='gray')
ax[0, 0].set_title('Original Image')
ax[0, 1].imshow(np.log(im_f_mag), cmap='magma')
ax[0, 1].set_title('Original FFT Magnitude (log)')
ax[1, 0].imshow(filtered_image, cmap='gray')
ax[1, 0].set_title('Filtered Image')
ax[1, 1].imshow(np.log(fim_f_mag), cmap='magma')
ax[1, 1].set_title('Filtered FFT Magnitude (log)')
plt.show()