多尺度的图像细节提升

论文的全名是"DARK IMAGE ENHANCEMENT BASED ON PAIRWISE TARGET CONTRAST AND MULTI-SCALE DETAIL BOOSTING"，论文核心的字段如下：

论文的核心思想类似于Retinex，使用了三个尺度的高斯模糊，再和原图做减法，获得不同程度的细节信息，然后通过一定的组合方式把这些细节信息融合到原图中，从而得到加强原图信息的能力：请参考这一篇博客介绍：SSE图像算法优化系列十二：多尺度的图像细节提升。

Python实现代码：

# -*- coding: utf-8 -*-

import cv2
import numpy as np
from numpy import uint8
from numpy import float32
from numpy import hstack

def multiScaleSharpen(img ,radius):
    h,w,chan = img.shape
 
    GaussBlue1 = np.zeros(img.shape,dtype = uint8)
    GaussBlue2 = np.zeros(img.shape, dtype=uint8)
    GaussBlue3 = np.zeros(img.shape, dtype=uint8)
    Dest_float_img = np.zeros(img.shape, dtype=float32)
    Dest_img = np.zeros(img.shape, dtype=uint8)
 
    w1 = 0.5
    w2 = 0.5
    w3 = 0.25
 
    GaussBlue1 = cv2.GaussianBlur(img,(radius,radius),1)
    GaussBlue2 = cv2.GaussianBlur(img,(radius*2-1,radius*2-1),2)
    GaussBlue3 = cv2.GaussianBlur(img,(radius*4-1,radius*4-1),4)
 
    for i in range(0,h):
        for j in range(0,w):
            for k in range(0,chan):
                Src = img.item(i,j,k)
                D1 = Src-GaussBlue1.item(i,j,k)
                D2 = GaussBlue1.item(i,j,k) - GaussBlue2.item(i,j,k)
                D3 = GaussBlue2.item(i,j,k) - GaussBlue3.item(i,j,k)
                if(D1 > 0):
                    sig = 1
                else:
                    sig = -1
                Dest_float_img.itemset((i,j,k),(1-w1*sig)*D1+w2*D2+w3*D3+Src)
 
    Dest_img = cv2.convertScaleAbs(Dest_float_img)
    return Dest_img
 
 
if __name__ == '__main__':
    img = cv2.imread("175_result.bmp")
    #img = cv2.imread("128.jpg")
 
    multiScaleSharpen_out = np.zeros(img.shape, dtype=uint8)
    multiScaleSharpen_out = multiScaleSharpen(img,5)#jishu
 
 
    #cv2.namedWindow("original image", cv2.WINDOW_NORMAL)
    #cv2.namedWindow("multiScaleSharpen image", cv2.WINDOW_NORMAL)
    #cv2.namedWindow("Compare image", cv2.WINDOW_NORMAL)
 
 
 
    #cv2.imshow("original image", img)
    #cv2.imshow("multiScaleSharpen image", multiScaleSharpen_out)
    #cv2.imshow("Compare image",hstack((img,multiScaleSharpen_out)))
    
    cv2.imwrite("multiScaleSharpen_175_result.bmp", multiScaleSharpen_out)
    #cv2.imwrite("multiScaleSharpen_128.jpg", multiScaleSharpen_out)
    #cv2.waitKey(0)  # 等待键盘触发事件，释放窗口

实验效果图

【转载】：https://blog.****.net/lz0499/article/details/82082481

                  https://blog.****.net/guyuealian/article/details/79065745

                  https://www.cnblogs.com/Imageshop/p/7895008.html