傅立葉轉換與雜訊分析

noise_fft2.py

import cv2
import numpy as np

# 椒鹽雜訊
def salt_pepper_noise(image, fraction, salt_vs_pepper):
    img = np.copy(image)
    size = img.size
    num_salt = np.ceil(fraction * size * salt_vs_pepper).astype('int')
    num_pepper = np.ceil(fraction * size * (1 - salt_vs_pepper)).astype('int')
    row, column = img.shape

    x = np.random.randint(0, column - 1, num_pepper)
    y = np.random.randint(0, row - 1, num_pepper)
    img[y, x] = 0

    x = np.random.randint(0, column - 1, num_salt)
    y = np.random.randint(0, row - 1, num_salt)
    img[y, x] = 255
    return img

def fftshow(title, img):
    f = np.fft.fft2(img)
    shifted = np.fft.fftshift(f)
    amp = np.abs(shifted)
    fimg = amp / np.max(amp) * 255
    cv2.imshow(title, fimg)

# 圖片來源：https://openhome.cc/Gossip/images/caterpillar.jpg
img = cv2.imread('caterpillar.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('caterpillar', img)
fftshow('caterpillar FFT', img)

noisy = salt_pepper_noise(img, 0.1 , 0.5)
cv2.imshow('noisy', noisy)

fftshow('Noisy FFT', noisy)

cv2.waitKey(0)
cv2.destroyAllWindows()