电子学报 ›› 2022, Vol. 50 ›› Issue (7): 1708-1721.DOI: 10.12263/DZXB.20201127

• 学术论文 • 上一篇    

基于景深先验引导与环境光优化的图像去雾

麻文刚, 张亚东, 郭进   

  1. 西南交通大学信息科学与技术学院,四川 成都 611756
  • 收稿日期:2020-10-13 修回日期:2021-05-31 出版日期:2022-07-25 发布日期:2022-07-30
  • 作者简介:麻文刚 男,1993年出生,甘肃天水人.西南交通大学博士生.主要研究方向为智能交通图像处理、通信系统等.E-mail: 1248564936@qq.com
    张亚东 男,1983年出生,河南商丘人.博士,西南交通大学讲师,硕士生导师.主要研究方向为计算机技术、系统可靠性与安全性理论及系统仿真测试等.E-mail: ydzhang@home.swjtu.edu.cn
    郭 进 男,1960年出生,四川成都人.博士,西南交通大学教授,博士生导师.主要研究方向为系统安全理论、安全苛求系统设计与验证等.E-mail: jguo_scce@swjtu.edu.cn
  • 基金资助:
    国家自然科学基金青年基金(61703349);中央高校基本科研业务费专项资金(2682017CX101);中国铁路总公司科技研究开发计划课题(N2018G062)

Image Dehazing Based on Priori Guidance of Depth of Field and Optimization of Ambient Light

MA Wen-gang, ZHANG Ya-dong, GUO Jin   

  1. School of Information Science and Technology,Southwest Jiao Tong University,Chengdu,Sichuan 611756,China
  • Received:2020-10-13 Revised:2021-05-31 Online:2022-07-25 Published:2022-07-30

摘要:

针对暗通道先验存在的边缘去雾不彻底及细节信息丢失等问题,提出了一种景深引导网络(Depth Guided Network,DGN)与环境光优化的图像去雾方法.首先,通过DGN中的图像去模糊分支与景深先验信息将有雾图像复原为初始清晰图像,同时提取图像中的景深信息,根据DGN中的景深细调网络恢复出景深图的边界与结构;然后,为了使细调后景深信息转化为图像特征,利用空间特征变换(Spatial Feature Transform,SFT)层将图像进行迭代变换,同时结合缩放及平移与初始清晰图像进行特征融合,根据景深引导将初始清晰图像进行优化;最后,为了进一步使复原图像具有适宜色彩与较高对比度,将动态环境光细化后用于图像去雾,进一步优化图像细节信息.实验表明:从主观对比来看,得到的复原图像平滑性较好,动态环境光可以增强图像的细节信息,得到对比度较高的图像;从客观对比分析来看,本文方法对合成有雾图像处理后的结构相似性与峰值信噪比的均值分别为88.78%和22.98 dB,对真实有雾图像处理后的细节强度与色彩还原度的均值分别为0.436 8和0.794.综合对比其他去雾方法,本文方法在性能指标最优的同时运行时间最短,能够适用于复杂环境场景的实时性去雾,且具有较好的鲁棒性.

关键词: 景深引导网络, 去模糊分支, 细调分支, 空间特征变换, 动态环境光, 图像去雾

Abstract:

Under the dark channel prior, there are incomplete edge defogs and loss of detailed information. Therefore, an image depthing method based on a depth guided network(DGN) and optimization of ambient light is proposed. First, the foggy image was restored to the initial clear image by using the image deblurring branch and depth of field prior information in the DGN. Meanwhile, the depth of field information in the image was extracted. The depth-of-field fine-tuning network in DGN was used to restore the boundaries and structures in the depth-of-field map. Then, the spatial feature transform(SFT) layer was used to transform the image iteratively. The purpose of transforming the fine-tuned depth information into image features was achieved. Moreover, zooming, panning, and initial clear image features were merged. Depth of field guidance was used to optimize the original image. Finally, the dynamic ambient light was refined and used for image defogging. The detailed information of the image was further optimized. The purpose of the restored image with suitable color and higher contrast was achieved. Experiments show that the smoothness of the restored image obtained from the subjective comparison is better. The dynamic ambient light optimization can enhance the detailed information of the image. Images with better contrast can be obtained. The mean value of SSIM and PSNR obtained by synthesizing foggy images is 88.78% and 22.98 dB, respectively. The average value of the detail intensity and the color reproduction obtained from the real foggy image are 0.436 8 and 0.794, respectively. When compared with other defogging methods, it has the best performance index and the shortest time. Furthermore, its advantage lies in better real-time defogging. Moreover, it has good robustness.

Key words: depth guided network, image deblurring branch, depth refinement branch, spatial feature transform, dynamic ambient light, image dehazing

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