DLT-Net: deep learning transmittance network for single image haze removal

Outdoor images taken in inclement weather conditions are often contaminated with colloidal particles and droplet in the atmosphere. These captured images are susceptible to low contrast, poor visibility, and color distortion, which is the reason for serious errors in digital image vision systems. Th...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2020-09, Vol.14 (6), p.1245-1253
Main Authors: Li, Bin, Zhao, Jingjuan, Fu, Hui
Format: Article
Language:English
Subjects:
Computer Imaging
Computer Science
Deep learning
Digital imaging
Fog
Haze
Image contrast
Image degradation
Image Processing and Computer Vision
Image restoration
Image segmentation
Low pass filters
Machine learning
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Transmittance
Visibility
Vision
Vision systems
Weather
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Summary:Outdoor images taken in inclement weather conditions are often contaminated with colloidal particles and droplet in the atmosphere. These captured images are susceptible to low contrast, poor visibility, and color distortion, which is the reason for serious errors in digital image vision systems. Therefore, defogging research has material significance for practical applications. In this paper, image dehazing is regarded as a mathematical inversion and image restoration process on the basis of fog image degradation model. The global atmospheric light A can be approximately estimated by combining Gaussian low-pass filtering with the single-threshold segmentation and binary tree method. And a deep learning transmittance network is adopted to modify transmittance. Comparison experimental results show that our method is effective in dealing with thick fog, complex scenes and multicolor images. In addition, our method is superior to four other state-of-the-art defogging methods in visual impact, universality and running speed. Graphic abstract The overall framework of our method
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-020-01665-9