周期噪声

周期噪声通常是在获取图像期间由电气或机电干扰产生的

def add_sin_noise(img, scale=1, angle=0):"""add sin noise for imageparam: img: input image, 1 channel, dtype=uint8param: scale: sin scaler, smaller than 1, will enlarge, bigger than 1 will shrinkparam: angle: angle of the rotationreturn: output_img: output image is [0, 1] image which you could use as mask or any you want to"""height, width = img.shape[:2]  # original image shape# convert all the angleif int(angle / 90) % 2 == 0:rotate_angle = angle % 90else:rotate_angle = 90 - (angle % 90)rotate_radian = np.radians(rotate_angle)    # convert angle to radian# get new image height and widthnew_height = int(np.ceil(height * np.cos(rotate_radian) + width * np.sin(rotate_radian)))new_width = int(np.ceil(width * np.cos(rotate_radian) + height * np.sin(rotate_radian))) # if new height or new width less than orginal height or width, the output image will be not the same shape as input, here set it rightif new_height < height:new_height = heightif new_width < width:new_width = width# meshgridu = np.arange(new_width)v = np.arange(new_height)u, v = np.meshgrid(u, v)# get sin noise image, you could use scale to make some difference, better you could add some shift
#     noise = abs(np.sin(u * scale))noise = 1 - np.sin(u * scale)# here use opencv to get rotation, better write yourself rotation functionC1 = cv2.getRotationMatrix2D((new_width/2.0, new_height/2.0), angle, 1)new_img = cv2.warpAffine(noise, C1, (int(new_width), int(new_height)), borderValue=0)# ouput image should be the same shape as input, so caculate the offset the output image and the new image# I make new image bigger so that it will cover all output imageoffset_height = abs(new_height - height) // 2offset_width = abs(new_width - width) // 2img_dst = new_img[offset_height:offset_height + height, offset_width:offset_width+width]output_img = normalize(img_dst)return output_img 

def spectrum_fft(fft):"""return FFT spectrum"""return np.sqrt(np.power(fft.real, 2) + np.power(fft.imag, 2))

# 周期噪声
img_ori = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH05/Fig0507(a)(ckt-board-orig).tif', 0) #直接读为灰度图像# 正弦噪声
noise = add_sin_noise(img_ori, scale=0.35, angle=-20)
img = np.array(img_ori / 255, np.float32)
img_noise = img + noise
img_noise = np.uint8(normalize(img_noise)*255)# 频率域中的其他特性
# FFT
img_fft = np.fft.fft2(img_noise.astype(np.float32))
# 中心化
fshift = np.fft.fftshift(img_fft)            # 将变换的频率图像四角移动到中心
# 中心化后的频谱
spectrum_fshift = spectrum_fft(fshift)
spectrum_fshift_n = np.uint8(normalize(spectrum_fshift) * 255)# 对频谱做对数变换
spectrum_log = np.log(1 + spectrum_fshift)plt.figure(figsize=(15, 10))
plt.subplot(121), plt.imshow(img_noise, 'gray'), plt.title('With Sine noise'), plt.xticks([]),plt.yticks([])
plt.subplot(122), plt.imshow(spectrum_log, 'gray'), plt.title('Spectrum'), plt.xticks([]),plt.yticks([])
# 在图像上加上箭头
plt.arrow(180, 180, 25, 30, width=5,length_includes_head=True, shape='full')
plt.arrow(285, 265, -25, -30, width=5,length_includes_head=True, shape='full')
plt.tight_layout()
plt.show()