Computer Science ›› 2020, Vol. 47 ›› Issue (8): 241-244.doi: 10.11896/jsjkx.200300068

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Single Image Defogging Method Based on Weighted Near-InFrared Image Fusion

ZHU Zhen1, HUANG Rui2, ZANG Tie-gang3, LU Shi-jun4   

  1. 1 School of Information Technology, Guangdong Engineering Polytechnic College, Guangzhou 510520, China
    2 School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
    3 School of Mechatronics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    4 School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:ZHU Zhen, born in 1980, master, associa-te professor.Her main research intere-sts include algorithm design and analysis, image processing, database application, and software engineering.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61274085), Guangdong Provincial Science and Technology Department Project(2014A010103008, 2016B090918021), Guangdong Provincial Education Department Project(2018GKQNCX009) and Guangdong Provincial Education Department Project(2016gzpp031).

Abstract: The traditional image defogging method has the problem that the image contrast in the fog-free area is too high, which causes the generated image visual effect is unnatural in some cases.In order to obtain natural and clear defogging images, a new single image defogging method based on weighted near-infrared(NIR) image fusion is proposed.Image contrast is restored by fusing the detail components of NIR images into visible images of the same scene.The NIR image is weighted using the transmission map to prevent excessive enhancement in the fog-free region.The experimental results show that compared with the traditional single image defogging methods, the proposed method can effectively restore the image contrast, does not excessively strengthen the fog-free area, and has higher PSNR and SSIM values.

Key words: Image defogging, Near-infrared image, Weighting, Image fusion, PSNR, SSIM

CLC Number: 

  • TP391
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