Journal
NEUROCOMPUTING
Volume 410, Issue -, Pages 363-373Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2020.06.041
Keywords
Non-local dehazing; Deep learning; Image restoration
Categories
Funding
- National Key Research and Develop Program of China [2017YFB0503004]
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Single image dehazing is one of the most challenging and important tasks in computer vision and image processing. In this paper, we propose a Non-local Dehazing Network (NLDN), which learns the mapping between hazy images and haze-free images. Our network architecture consists three components: the first is full point-wise convolutional part, which extracts Non-local statistical regularities; the second is feature combination part, which learns the spatial relation of statistical regularities; the third is reconstruction part, which recovers the haze-free image by the features extracted from the second part. By using these three components, we obtain a high quality dehazing result. Experimental results show that our method performs favorably against other state-of-the-art methods on both synthetic dataset and real-world images. (C) 2020 Elsevier B.V. All rights reserved.
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