如何将 LPF 和 HPF 应用于 FFT(傅立叶变换) [英] How to apply a LPF and HPF to a FFT (Fourier transform)

问题描述

我需要将 HPF 和 LPF 应用于傅立叶图像并执行逆变换，然后比较它们.我执行以下算法，但没有任何结果:

I need to apply HPF and LPF to the Fourier Image and perform the inverse transformation, and compare them. I do the following algorithm, but nothing comes out:

img = cv2.imread('pic.png')

f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20 * np.log(np.abs(fshift))

# need to add HPF and LPF 
hpf = ...
lpf = ... # maybe 1 - hpf ?

# inverse 
result = (lpf + (1 + alpha) * hpf)

你能告诉我怎么做吗?

推荐答案

您使用白色圆圈黑色背景并将其应用于 FFT 幅度以进行低通滤波器.高通滤波器是低通滤波器的反极性——白底黑圈.您可以减轻铃声"通过对圆应用高斯滤波器来影响结果.这是低通滤波器的示例.

You use a white circle black background and apply it to the FFT magnitude to do a low pass filter. The high pass filter is the reverse polarity of the low pass filter -- black circle on white background. You can mitigate the "ringing" effect in the result by applying a Gaussian filter to the circle. Here is an example of a low pass filter.

输入:

import numpy as np
import cv2

# read input and convert to grayscale
img = cv2.imread('lena.png')

# do dft saving as complex output
dft = np.fft.fft2(img, axes=(0,1))

# apply shift of origin to center of image
dft_shift = np.fft.fftshift(dft)

# generate spectrum from magnitude image (for viewing only)
mag = np.abs(dft_shift)
spec = np.log(mag) / 20

# create circle mask
radius = 32
mask = np.zeros_like(img)
cy = mask.shape[0] // 2
cx = mask.shape[1] // 2
cv2.circle(mask, (cx,cy), radius, (255,255,255), -1)[0]

# blur the mask
mask2 = cv2.GaussianBlur(mask, (19,19), 0)

# apply mask to dft_shift
dft_shift_masked = np.multiply(dft_shift,mask) / 255
dft_shift_masked2 = np.multiply(dft_shift,mask2) / 255


# shift origin from center to upper left corner
back_ishift = np.fft.ifftshift(dft_shift)
back_ishift_masked = np.fft.ifftshift(dft_shift_masked)
back_ishift_masked2 = np.fft.ifftshift(dft_shift_masked2)


# do idft saving as complex output
img_back = np.fft.ifft2(back_ishift, axes=(0,1))
img_filtered = np.fft.ifft2(back_ishift_masked, axes=(0,1))
img_filtered2 = np.fft.ifft2(back_ishift_masked2, axes=(0,1))

# combine complex real and imaginary components to form (the magnitude for) the original image again
img_back = np.abs(img_back).clip(0,255).astype(np.uint8)
img_filtered = np.abs(img_filtered).clip(0,255).astype(np.uint8)
img_filtered2 = np.abs(img_filtered2).clip(0,255).astype(np.uint8)


cv2.imshow("ORIGINAL", img)
cv2.imshow("SPECTRUM", spec)
cv2.imshow("MASK", mask)
cv2.imshow("MASK2", mask2)
cv2.imshow("ORIGINAL DFT/IFT ROUND TRIP", img_back)
cv2.imshow("FILTERED DFT/IFT ROUND TRIP", img_filtered)
cv2.imshow("FILTERED2 DFT/IFT ROUND TRIP", img_filtered2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# write result to disk
cv2.imwrite("lena_dft_numpy_mask.png", mask)
cv2.imwrite("lena_dft_numpy_mask_blurred.png", mask2)
cv2.imwrite("lena_dft_numpy_roundtrip.png", img_back)
cv2.imwrite("lena_dft_numpy_lowpass_filtered1.png", img_filtered)
cv2.imwrite("lena_dft_numpy_lowpass_filtered2.png", img_filtered2)

遮罩 1(低通滤波器):

Mask 1 (low pass filter):

遮罩 2(低通滤波器模糊):

Mask 2 (low pass filter blurred):

结果 1:

结果 2(减少振铃):

Result 2 (reduced ringing):

添加

这里是高通滤波器处理(边缘检测器).

Here is the high pass filter processing (edge detector).

import numpy as np
import cv2

# read input and convert to grayscale
#img = cv2.imread('lena_gray.png', cv2.IMREAD_GRAYSCALE)
img = cv2.imread('lena.png')

# do dft saving as complex output
dft = np.fft.fft2(img, axes=(0,1))

# apply shift of origin to center of image
dft_shift = np.fft.fftshift(dft)

# generate spectrum from magnitude image (for viewing only)
mag = np.abs(dft_shift)
spec = np.log(mag) / 20

# create white circle mask on black background and invert so black circle on white background
radius = 32
mask = np.zeros_like(img)
cy = mask.shape[0] // 2
cx = mask.shape[1] // 2
cv2.circle(mask, (cx,cy), radius, (255,255,255), -1)[0]
mask = 255 - mask

# blur the mask
mask2 = cv2.GaussianBlur(mask, (19,19), 0)

# apply mask to dft_shift
dft_shift_masked = np.multiply(dft_shift,mask) / 255
dft_shift_masked2 = np.multiply(dft_shift,mask2) / 255


# shift origin from center to upper left corner
back_ishift = np.fft.ifftshift(dft_shift)
back_ishift_masked = np.fft.ifftshift(dft_shift_masked)
back_ishift_masked2 = np.fft.ifftshift(dft_shift_masked2)


# do idft saving as complex output
img_back = np.fft.ifft2(back_ishift, axes=(0,1))
img_filtered = np.fft.ifft2(back_ishift_masked, axes=(0,1))
img_filtered2 = np.fft.ifft2(back_ishift_masked2, axes=(0,1))

# combine complex real and imaginary components to form (the magnitude for) the original image again
# multiply by 3 to increase brightness
img_back = np.abs(img_back).clip(0,255).astype(np.uint8)
img_filtered = np.abs(3*img_filtered).clip(0,255).astype(np.uint8)
img_filtered2 = np.abs(3*img_filtered2).clip(0,255).astype(np.uint8)


cv2.imshow("ORIGINAL", img)
cv2.imshow("SPECTRUM", spec)
cv2.imshow("MASK", mask)
cv2.imshow("MASK2", mask2)
cv2.imshow("ORIGINAL DFT/IFT ROUND TRIP", img_back)
cv2.imshow("FILTERED DFT/IFT ROUND TRIP", img_filtered)
cv2.imshow("FILTERED2 DFT/IFT ROUND TRIP", img_filtered2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# write result to disk
cv2.imwrite("lena_dft_numpy_mask_highpass.png", mask)
cv2.imwrite("lena_dft_numpy_mask_highpass_blurred.png", mask2)
cv2.imwrite("lena_dft_numpy_roundtrip.png", img_back)
cv2.imwrite("lena_dft_numpy_highpass_filtered1.png", img_filtered)
cv2.imwrite("lena_dft_numpy_highpass_filtered2.png", img_filtered2)

遮罩 1(高通滤波器):

Mask 1 (high pass filter):

遮罩 2(高通滤波器模糊):

Mask 2 (high pass filter blurred):

结果 1:

结果 2:

附加 2

这里是高升压滤波器处理.高增强滤波器是一种锐化滤波器，只是 1 + 分数 * 高通滤波器.注意这里的高通滤波器是在 0 到 1 而不是 0 到 255 的范围内创建的，以便于使用和解释.

Here is the high boost filter processing. The high boost filter, which is a sharpening filter, is just 1 + fraction * high pass filter. Note the high pass filter here is in created in the range 0 to 1 rather than 0 to 255 for ease of use and explanation.

import numpy as np
import cv2

# read input and convert to grayscale
#img = cv2.imread('lena_gray.png', cv2.IMREAD_GRAYSCALE)
img = cv2.imread('lena.png')

# do dft saving as complex output
dft = np.fft.fft2(img, axes=(0,1))

# apply shift of origin to center of image
dft_shift = np.fft.fftshift(dft)

# generate spectrum from magnitude image (for viewing only)
mag = np.abs(dft_shift)
spec = np.log(mag) / 20

# create white circle mask on black background and invert so black circle on white background
# as highpass filter
radius = 32
mask = np.zeros_like(img, dtype=np.float32)
cy = mask.shape[0] // 2
cx = mask.shape[1] // 2
cv2.circle(mask, (cx,cy), radius, (1,1,1), -1)[0]
mask = 1 - mask

# high boost filter (sharpening) = 1 + fraction of high pass filter
mask = 1 + 0.5*mask 

# blur the mask
mask2 = cv2.GaussianBlur(mask, (19,19), 0)

# apply mask to dft_shift
dft_shift_masked = np.multiply(dft_shift,mask)
dft_shift_masked2 = np.multiply(dft_shift,mask2)

# shift origin from center to upper left corner
back_ishift = np.fft.ifftshift(dft_shift)
back_ishift_masked = np.fft.ifftshift(dft_shift_masked)
back_ishift_masked2 = np.fft.ifftshift(dft_shift_masked2)

# do idft saving as complex output
img_back = np.fft.ifft2(back_ishift, axes=(0,1))
img_filtered = np.fft.ifft2(back_ishift_masked, axes=(0,1))
img_filtered2 = np.fft.ifft2(back_ishift_masked2, axes=(0,1))

# combine complex real and imaginary components to form (the magnitude for) the original image again
img_back = np.abs(img_back).clip(0,255).astype(np.uint8)
img_filtered = np.abs(img_filtered).clip(0,255).astype(np.uint8)
img_filtered2 = np.abs(img_filtered2).clip(0,255).astype(np.uint8)

cv2.imshow("ORIGINAL", img)
cv2.imshow("SPECTRUM", spec)
cv2.imshow("MASK", mask)
cv2.imshow("MASK2", mask2)
cv2.imshow("ORIGINAL DFT/IFT ROUND TRIP", img_back)
cv2.imshow("FILTERED DFT/IFT ROUND TRIP", img_filtered)
cv2.imshow("FILTERED2 DFT/IFT ROUND TRIP", img_filtered2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# write result to disk
cv2.imwrite("lena_dft_numpy_roundtrip.png", img_back)
cv2.imwrite("lena_dft_numpy_highboost_filtered1.png", img_filtered)
cv2.imwrite("lena_dft_numpy_highboost_filtered2.png", img_filtered2)

结果 1:

结果 2:

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