| Autor: |
Hong Xu Yuan, Zhiwu Liao, Rui Xin Wang, Xinceng Dong, Tao Liu, Wu Dan Long, Qing Jin Wei, Ya Jie Xu, Yong Yu, Peng Chen, Rong Hou |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Physics, Vol 10 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2296-424X |
| DOI: |
10.3389/fphy.2022.828804 |
| Popis: |
Deep neural networks (DNNs) with long-range dependence (LRD) have attracted more and more attention recently. However, LRD of DNNs is proposed from the view on gradient disappearance in training, which lacks theory analysis. In order to prove LRD of foggy images, the Hurst parameters of over 1,000 foggy images in SOTS are computed and discussed. Then, the Residual Dense Block Group (RDBG), which has additional long skips among two Residual Dense Blocks to fit LRD of foggy images, is proposed. The Residual Dense Block Group can significantly improve the details of dehazing image in dense fog and reduce the artifacts of dehazing image. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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