高斯差分帶通濾波

帶通濾波器會衰減感興趣範圍（頻帶）之外的訊號頻率。在影像分析中，它們可以用於對影像去噪，同時減少低頻偽影，例如不均勻照明。帶通濾波器可用於尋找影像特徵，例如斑點和邊緣。

將帶通濾波器應用於影像的一種方法是從較少模糊的影像中減去使用高斯核心模糊的影像。此範例顯示了高斯差分方法在帶通濾波的兩種應用。

對影像去噪並減少陰影

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()

增強影像中的邊緣

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()