基于线性模型的自适应优化去雾算法

针对线性传输算法中透射率和大气光估计不足问题，提出一种基于线性模型的自适应优化去雾算法。利用边缘信息模型来增强初始透射率图的细节信息，使得复原后图像边缘区域细节更丰富；根据暗通道先验，得到自适应优化透射率，更好地处理包含景深区域图像；采用局部大气光估计方法代替四叉树方法，避免大气光估计不准确问题，并结合物理模型恢复图像。仿真实验在matlab2014中进行，实验结果表明，该算法具有较好的有效性和时效性。

Adaptive optimization defogging algorithm based on linear model

Aiming at the problem of insufficient estimate of transmittance and atmospheric light in linear transformation algorithm,an adaptive optimization defogging algorithm based on linear model was proposed.First,the edge information model was used to enhance the detailed information of the initial transmittance image,so that the edge region details of the restored image were richer.Then,an adaptive optimization transmittance was obtained to better process the image including the depth of field region by the dark channel prior.Finally,the local atmospheric light estimate method was used instead of the quadtree method to avoid the inaccuracy of atmospheric light estimate,and the image was restored by combining with the physical model.The simulation experiment was carried out in matlab2014,and the experimental results show that the proposed algorithm has good validity and timeliness.