混合残差学习与导向滤波算法在图像去雾中的应用

陈清江; 张雪

doi:10.3788/OPE.20192712.2702

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混合残差学习与导向滤波算法在图像去雾中的应用

信息科学 | 更新时间：2020-07-09

- 混合残差学习与导向滤波算法在图像去雾中的应用
- Application of hybrid residual learning and guided filtering algorithm in image defogging
- 光学精密工程 2019年27卷第12期页码：2702-2712
- 作者机构：
  
  西安建筑科技大学理学院, 陕西西安 710055
- 作者简介：
  
  [ "陈清江(1966-)，男，河南信阳人，博士，教授，2003年于信阳师范学院获硕士学位，2006年于西安交通大学获博士学位，主要从事小波分析，图像处理与信号处理方面的研究。E-mail：qjchen66xytu@126.com" ]
  [ "张雪(1995-)，女，陕西宝鸡人，硕士研究生，2017年于商洛学院获得理学学士学位，主要从事小波分析，图像处理与信号处理方面的研究。E-mail：2445161583@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61403298);陕西省自然科学基金资助项目(2015JM1024);陕西省教育厅专项科研计划项目(2013JK0586)
- DOI：10.3788/OPE.20192712.2702
  中图分类号： TP391.4
- 收稿日期：2019-06-12，
  
  录用日期：2019-7-8，
  
  纸质出版日期：2019-12-25
- 稿件说明：
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陈清江, 张雪. 混合残差学习与导向滤波算法在图像去雾中的应用[J]. 光学精密工程, 2019,27(12):2702-2712.

Qing-jiang CHEN, Xue ZHANG. Application of hybrid residual learning and guided filtering algorithm in image defogging[J]. Optics and precision engineering, 2019, 27(12): 2702-2712.
陈清江, 张雪. 混合残差学习与导向滤波算法在图像去雾中的应用[J]. 光学精密工程, 2019,27(12):2702-2712. DOI： 10.3788/OPE.20192712.2702.

Qing-jiang CHEN, Xue ZHANG. Application of hybrid residual learning and guided filtering algorithm in image defogging[J]. Optics and precision engineering, 2019, 27(12): 2702-2712. DOI： 10.3788/OPE.20192712.2702.

摘要

为解决雾天场景图像恢复过程中图像清晰度和对比度下降的问题，提出了一种结合残差学习和导向滤波的单幅图像去雾算法。使用雾天图像与对应的清晰图像构建残差网络；采用多尺度卷积提取更多细节的雾霾特征；利用导向滤波各向异性的优点，对残差网络去雾后的图像进行滤波以保持图像边缘特性，得到更加清晰的无雾图像。通过与DCP算法、CAP算法、SRCNN算法、DehazeNet算法和MSCNN算法相比，在合成雾天图像上，峰值信噪比值最高达到31.951 8 dB，结构相似度值最高达到0.979 6，在自然雾天图像上的运行时间最低达到了0.4 s，主观评价和客观评价均优于其它对比算法。实验结果表明，所提去雾算法不仅去雾效果较优，而且速度较快，具有较强的实用价值。

Abstract

To solve the problem of image clarity and contrast degradation in fog scene image restoration

a single image defogging algorithm based on residual learning and guided filtering was proposed. A residual network was first constructed by using foggy images and corresponding clear images. Multi-scale convolution was then used to extract more detailed haze features. Taking advantage of the anisotropy of the guided filter

the algorithm then obtained a clearer fog-free image after the residual network was filtered to maintain image edge characteristics. Experiments produced the following results as compared with the dark channel prior

CAP

super-resolution convolutionalneuralnetwork

DehazeNet

and multi-scale convolutional neural network algorithms.On synthetic foggy images

the peak signal-to-noise ratio reacheda maximum of 27.840 3 dB

the structural similarity index measurereacheda maximum of 0.979 6

and the runtime on natural foggy images was as low as 0.4 s.In addition

the subjective and objective evaluations proved to be better than those of the other comparison algorithms.Thus

the proposed defogging algorithm not only produces a better defogging effectbut is also faster

there by offering a greater practical valuefor defogging applications than the other algorithms.

关键词

Keywords

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