使用 Python 对照片进行去噪

Question

我有以下图片，它是一本旧书的扫描副本。我想去除由于扫描旧照片而产生的背景噪音（有点偏红）。

更新：

应用opencv 后，按照opencv doc 中的参数设置，我得到以下输出。

请帮助解决这个问题。

我正在使用的代码：

import numpy as np
import cv2
from matplotlib import pyplot as plt

def display_image_in_actual_size(im_data):

    dpi = 80
    height, width, depth = im_data.shape

    # What size does the figure need to be in inches to fit the image?
    figsize = width / float(dpi), height / float(dpi)

    # Create a figure of the right size with one axes that takes up the full figure
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes([0, 0, 1, 1])

    # Hide spines, ticks, etc.
    ax.axis('off')

    # Display the image.
    ax.imshow(im_data, cmap='gray')

    plt.show()

img = cv2.imread('scan03.jpg')

dst = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)

display_image_in_actual_size(img)
display_image_in_actual_size(dst)

Answer 1

一些具有接近阈值像素值的像素的颜色会受到影响，但这取决于任务，这是一种解决方案，您可以将阈值调整为适合您任务的值，也可以移除中值过滤器，或者减小 sigma 值(5)，如果它严重影响文本，您可能会有一些不想要的噪音，但文本将是可读的。

import numpy as np
import matplotlib.pyplot as plt
import cv2
# Read Image
img = cv2.imread('input.jpg')
# BGR --> RGB
RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# BGR --> Gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Set thresholds
th_white = 210
th_black = 85
# copy original gray
mask_white = gray.copy()
mask_black = gray.copy()
# Thresholding
mask_white[mask_white<th_white] = 0
mask_black[mask_black<th_black] = 0
mask_white[mask_white>=th_white] = 255
mask_black[mask_black>=th_black] = 255
# Median Filtering (you can remove if the text is not readable)
median_white = cv2.medianBlur(mask_white,5)
median_black = cv2.medianBlur(mask_black,5)
# Mask 3 channels
mask_white_3 = np.stack([median_white, median_white, median_white], axis=2)
mask_black_3 = np.stack([median_black, median_black, median_black], axis=2)
# Masking the image(in RGB)
result1 = np.maximum(mask_white_3, RGB)
result2 = np.minimum(mask_black_3, result1)
# Visualize the results
plt.imshow(result2)
plt.axis('off')
plt.show()

Answer 2

opencv 库有几个降噪函数。

您可以找到阅读示例here