一种改进的结合边缘检测的去雾新方法

暗原色去雾算法复原的图像会出现边缘效应,为此提出了一种改进的基于暗原色理论的去雾算法.首先计算出图像的暗色图,并使用小波分解算法将暗色图像向下采样一次;然后在小波分解的平滑部分计算大气光和图像的暗原色,提取出平滑部分的边缘信息,并使用形态学对其进行膨胀获得相应的二值边缘图像;接着对二值边缘图像所在区域的暗原色及非边缘处的暗原色分别进行优化;最后,将优化后的暗原色使用小波重构算法向上采样,并据此计算出图像透射率,结合去雾模型复原到清晰的无雾图像.实验证明,该方法在很好地去除边缘效应的同时,极大地减少了算法时间复杂度,满足实时要求.

An Improved Defogging Algorithm Combined with Edge Detection

Abstract： The images restored by using the dark channel prior theory have haze edges. Therefore, a novel improved image clearness method based on dark channel prior was proposed to solve this insufficient. First, dark color image was obtained and decomposed by wavelet. Next, the smooth portion from wavelet decomposition was used as the target image, in which global atmospheric optical A and the dark channel value were calculated, and then, the edge information of target image was extracted and dilated. After that, according to the expanded binary edge image, the dark channel value in edges and that in smooth were optimized, respectively. Finally, according to the transmission map calculated using the optimized dark channel values, the fog free image was obtained by using the physical model. The experiment results show that this method can effectively restore the fogging images and greatly reduce the time complexity brought by the soft matting algorithm.