图像去雾算法

1.代码

import cv2 import numpy as np def zmMinFilterGray(src, r=7): '''最小值滤波，r是滤波器半径''' '''if r <= 0: return src h, w = src.shape[:2] I = src res = np.minimum(I , I[[0]+range(h-1) , :]) res = np.minimum(res, I[range(1,h)+[h-1], :]) I = res res = np.minimum(I , I[:, [0]+range(w-1)]) res = np.minimum(res, I[:, range(1,w)+[w-1]]) return zmMinFilterGray(res, r-1)''' return cv2.erode(src, np.ones((2 * r + 1, 2 * r + 1))) # 使用opencv的erode函数更高效 def guidedfilter(I, p, r, eps): '''引导滤波，直接参考网上的matlab代码''' height, width = I.shape m_I = cv2.boxFilter(I, -1, (r, r)) m_p = cv2.boxFilter(p, -1, (r, r)) m_Ip = cv2.boxFilter(I * p, -1, (r, r)) cov_Ip = m_Ip - m_I * m_p m_II = cv2.boxFilter(I * I, -1, (r, r)) var_I = m_II - m_I * m_I a = cov_Ip / (var_I + eps) b = m_p - a * m_I m_a = cv2.boxFilter(a, -1, (r, r)) m_b = cv2.boxFilter(b, -1, (r, r)) return m_a * I + m_b def getV1(m, r, eps, w, maxV1): # 输入rgb图像，值范围[0,1] '''计算大气遮罩图像V1和光照值A, V1 = 1-t/A''' V1 = np.min(m, 2) # 得到暗通道图像 V1 = guidedfilter(V1, zmMinFilterGray(V1, 7), r, eps) # 使用引导滤波优化 bins = 2000 ht = np.histogram(V1, bins) # 计算大气光照A d = np.cumsum(ht[0]) / float(V1.size) for lmax in range(bins - 1, 0, -1): if d[lmax] <= 0.999: break A = np.mean(m, 2)[V1 >= ht[1][lmax]].max() V1 = np.minimum(V1 * w, maxV1) # 对值范围进行限制 return V1, A def deHaze(m, r=81, eps=0.001, w=0.95, maxV1=0.80, bGamma=False): Y = np.zeros(m.shape) V1, A = getV1(m, r, eps, w, maxV1) # 得到遮罩图像和大气光照 for k in range(3): Y[:, :, k] = (m[:, :, k] - V1) / (1 - V1 / A) # 颜色校正 Y = np.clip(Y, 0, 1) if bGamma: Y = Y ** (np.log(0.5) / np.log(Y.mean())) # gamma校正,默认不进行该操作 return Y if __name__ == '__main__': m = deHaze(cv2.imread('C:/Users/55192/Desktop/1.bmp') / 255.0) * 255 cv2.imwrite('C:/Users/55192/Desktop/2.bmp', m)

2.结果

参考