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http://hdl.handle.net/10397/105516
Title: | Simplified non-locally dense network for single-image dehazing | Authors: | Chen, Z Hu, Z Sheng, B Li, P Kim, J Wu, E |
Issue Date: | Oct-2020 | Source: | Visual computer, Oct.-Dec. 2020, v. 36, no. 10-12, p. 2189-2200 | Abstract: | Single-image dehazing is an ill-posed problem. Most previous methods focused on estimating intermediate parameters for input hazy images. In this paper, we propose a novel end-to-end Simplified Non-locally Dense Network (SNDN) which does not rely on intermediate parameters. To capture long-range dependencies, we propose a Simplified Non-local Dense Block (SNDB) which is lightweight and outperforms traditional non-local method. Our SNDB will be embedded into a densely connected encoder–decoder network. To avoid gradients vanishing problem, we propose a simple branch network which only have five convolution layers. The effectiveness of our proposed network is proved through ablation experiment. In addition, we enhanced our training set by synthesizing colored hazy images, which helps restore the original color of the hazy image. The experimental results demonstrate that our network have better performance than most of the pervious state-of-the-art methods. | Keywords: | Dense Non-local Single-image dehazing |
Publisher: | Springer | Journal: | Visual computer | ISSN: | 0178-2789 | EISSN: | 1432-2315 | DOI: | 10.1007/s00371-020-01929-y | Rights: | © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00371-020-01929-y. |
Appears in Collections: | Journal/Magazine Article |
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Li_Simplified_Non-Locally_Dense.pdf | Pre-Published version | 8.91 MB | Adobe PDF | View/Open |
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