通过仿真自动色阶算法，发现其去雾效果十分明显，并且速度快于暗通道算法。

python实现：

#!python3.6

import numpy as np
import cv2

def ComputeHist(img):
    h,w = img.shape
    hist, bin_edge = np.histogram(img.reshape(1,w*h), bins=list(range(257)))
    return hist
    
def ComputeMinLevel(hist, rate, pnum):
    sum = 0
    for i in range(256):
        sum += hist[i]
        if (sum >= (pnum * rate * 0.01)):
            return i
            
def ComputeMaxLevel(hist, rate, pnum):
    sum = 0
    for i in range(256):
        sum += hist[255-i]
        if (sum >= (pnum * rate * 0.01)):
            return 255-i
            
def LinearMap(minlevel, maxlevel):
    if (minlevel >= maxlevel):
        return []
    else:
        newmap = np.zeros(256)
        for i in range(256):
            if (i < minlevel):
                newmap[i] = 0
            elif (i > maxlevel):
                newmap[i] = 255
            else:
                newmap[i] = (i-minlevel)/(maxlevel-minlevel) * 255
        return newmap
        
def CreateNewImg(img):
    h,w,d = img.shape
    newimg = np.zeros([h,w,d])
    for i in range(d):
        imgmin = np.min(img[:,:,i])
        imgmax = np.max(img[:,:,i])
        imghist = ComputeHist(img[:,:,i])
        minlevel = ComputeMinLevel(imghist, 8.3, h*w)
        maxlevel = ComputeMaxLevel(imghist, 2.2, h*w)
        newmap = LinearMap(minlevel,maxlevel)
        # print(minlevel, maxlevel)
        if (newmap.size ==0 ):
            continue
        for j in range(h):
            newimg[j,:,i] = newmap[img[j,:, i]]
    return newimg
    
    
    
if __name__ == '__main__':
    img = cv2.imread('2017/2017_0_0_1000_.jpg',1)
    newimg = CreateNewImg(img)
    cv2.namedWindow('img',0)
    cv2.imshow('img', img)
    cv2.namedWindow('newimg',0)
    cv2.imshow('newimg', newimg/255)
    cv2.waitKey(0)