计算机工程

• 图形图像处理 • 上一篇    下一篇

基于变分模型的图像去雾方法

刘建磊1,2   

  1. (1.山东交通学院 轨道交通学院,济南 250357; 2.山东大学 控制科学与工程学院,济南 250061)
  • 收稿日期:2016-12-05 出版日期:2017-11-15 发布日期:2017-11-15
  • 作者简介:刘建磊(1981—),男,副教授、博士,主研方向为数字图像处理、智能信息处理。
  • 基金项目:
    中国博士后科学基金(2015M572034);山东省高等学校科技计划项目(J15LN14); 山东省自然科学基金(ZR2015FL016)。

Image Dehazing Method Based on Variational Model

LIU Jianlei 1,2   

  1. (1.School of Rail Transportation,Shandong Jiaotong University,Jinan 250357,China; 2.College of Control Science and Engineering,Shandong University,Jinan 250061,China)
  • Received:2016-12-05 Online:2017-11-15 Published:2017-11-15

摘要: 基于雾天图像成像模型的去雾方法在天空区域易产生失真现象,并且存在边缘处透射率计算不准确的问题,为此,提出一种图像去雾方法。该方法基于变分模型构建含有数据项、平滑项和边缘保持项的能量泛函,利用梯度下降流法最小化该能量泛函以达到透射率的精确求解,根据已获取的透射率值和雾天图像复原理论实现图像的精确复原。实验结果表明,该方法在天空区域和边缘区域的去雾效果优于传统方法,具有更小的均方误差值和更大的结构相似度值。

关键词: 透射率, 梯度下降流, 变分模型, 能量泛函, 暗原色先验

Abstract: The fog removal method based on fog image imaging model exists distortion phenomenon in the sky region and the calculation of transmissivity at the edge is not accurate.In order to solve this problem,this paper proposes an image dehazing method.The method constructs energy functional which consists of data term,smoothness term,and edge-preserving term based on variational model.The energy functional is minimized by the gradient descent flow method to achieve accurate transmissivity.According to the acquired transmissivity values and fog image restoration theory,the accurate restoration of the image is realized.Experimental results show that the dehazing effect of the proposed method is superior to that of the traditional method in the sky region and the edge region,and has smaller mean square error value and greater structural similarity value.

Key words: transmissivity, gradient descent flow, variational model, energy functional, dark channel prior

中图分类号: