如何使用OpenCV消除扫描图像中的阴影? [英] How to remove shadow from scanned images using OpenCV?

问题描述

我想在使用OpenCV进行图像二值化之前去除阴影.我已经尝试过Otsu方法和自适应阈值，但是对于阴影区域较大的图像，这两种方法将无法获得良好的效果.

I'd like to remove shadow before image binarization using OpenCV. I've tried Otsu Method and adaptive thresholding, however for images where there are large regions of shadow, these two methods will not give good results.

有更好的解决方案吗?预先感谢.

Any better solutions? Thanks in advance.

] ] 推荐答案

由于您未指定任何语言，因此我假设使用Python进行说明.

Since you didn't specify any language, I'll assume Python to illustrate.

一个不错的起点可能是采用我在此答案中显示的方法，并将其扩展为可用于多个渠道.

A decent starting point might be taking the approach I show in this answer and expand it to work with multiple channels.

类似的东西

import cv2
import numpy as np

img = cv2.imread('shadows.png', -1)

rgb_planes = cv2.split(img)

result_planes = []
result_norm_planes = []
for plane in rgb_planes:
    dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8))
    bg_img = cv2.medianBlur(dilated_img, 21)
    diff_img = 255 - cv2.absdiff(plane, bg_img)
    norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
    result_planes.append(diff_img)
    result_norm_planes.append(norm_img)

result = cv2.merge(result_planes)
result_norm = cv2.merge(result_norm_planes)

cv2.imwrite('shadows_out.png', result)
cv2.imwrite('shadows_out_norm.png', result_norm)

未归一化的结果如下:

以及归一化结果:

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