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Adaptive Dehaze Method for Aerial Image Processing

  • Rong-qin Xu
  • Sheng-hua Zhong
  • Gaoyang Tang
  • Jiaxin Wu
  • Yingying Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

Remote sensing images or images collected by unmanned aerial vehicles in the hazy weather are easily interfered by scattering effect generated by atmospheric particulate matter. The terrible interference will not only lead to the images quality seriously degraded, but also result in a bad effect on the process of images feature extraction and images feature matching. In this paper, by proposing an effective adaptive dehaze method, we compare the statistical results of feature detection and matching based on Scale-invariant feature transform (SIFT) detector and descriptor before and after haze removal. And we also provide the comparisons of image stitching task. The experimental results show that, after the haze removal is implemented on hazy images, more SIFT feature keypoints and SIFT matching keypoints will be extracted, which is also beneficial to images stitching. Moreover, the proposed adaptive method performs better than the original dehaze method.

Keywords

Adaptive haze removal SIFT detector and descriptor Image matching Image stitching Kernel graph cuts Random walk 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61502311, 61602314), the Natural Science Foundation of Guangdong Province (No. 2016A030310053, 2016A030313043), the Shenzhen high-level overseas talents program, and the Tencent “Rhinoceros Bird” - Scientific Research Foundation for Young Teachers of Shenzhen University.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rong-qin Xu
    • 1
  • Sheng-hua Zhong
    • 1
  • Gaoyang Tang
    • 1
  • Jiaxin Wu
    • 1
  • Yingying Zhu
    • 1
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China

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