吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于特征融合的快速图像去雾方法

马书一, 郝巧红, 管庆吉, 齐妙   

  1. 东北师范大学 计算机科学与信息技术学院, 长春 130117
  • 收稿日期:2015-05-12 出版日期:2016-01-26 发布日期:2016-01-19
  • 通讯作者: 齐妙 E-mail:qim801@nenu.edu.cn

Fast Image Dehazing Method Based on Feature Fusion

MA Shuyi, HAO Qiaohong, GUAN Qingji, QI Miao   

  1. School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
  • Received:2015-05-12 Online:2016-01-26 Published:2016-01-19
  • Contact: QI Miao E-mail:qim801@nenu.edu.cn

摘要:

针对图像去雾问题, 提出一种基于特征融合的快速单幅图像去雾方法, 解决了暗通道方法存在的块效应问题. 该方法先采用基于K均值聚类的暗通道先验求得粗尺度下的透射率, 再通过分析雾对成像的影响, 提取有雾图像自身能反映景深变化的饱和度作为细尺度的透射率, 最后通过图像融合技术得到精确的透射率. 通过对
各种真实有雾场景进行测试的实验结果表明, 该方法简单且有效, 能得到理想的去雾效果.

关键词: 图像去雾, K均值聚类, 多尺度, 特征融合

Abstract:

Aiming at the problem of image processing, we proposed a fast single image dehazing method based on feature fusion, and solved the problem of the block effect of dark channel method. By means of the dark channel prior based on K-means clustering, the coarsescale transmission was obtained. By analyzing the effect of haze on imaging, we extracted the saturation of hazy image itself as finescale transmission, which could reflect the change of scene depth effectively. Finally, we obtained accurate transmission via image fusion technique. Through the test of a variety of realscene images, the experiment results show that the method is simple and effective, and can get ideal dehazing result.

Key words: image dehazing, K-means clustering, multiscale, feature fusion

中图分类号: 

  • TP391.41