| Autor: |
Qiaosi Yi, Aiwen Jiang, Juncheng Li, Jianyi Wan, Mingwen Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 8, Pp 54514-54521 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2020.2981491 |
| Popis: |
In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years. Most of existing methods are grouped into physical prior based and non-physical data-driven based categories. However, image dehazing is a challenging ill-conditioned and inherently ambiguous problem. Due to random distribution and concentration of haze, color distortion and excessive brightness often happen in physical prior based methods. Defects on high-frequency details' recovery are not solved well in non-physical data-driven methods. Therefore, to overcome these obstacles, in this paper, we have proposed an effective progressive back-traced dehazing network based on multi-resolution recurrent reconstruction strategies. A kind of irregular multi-scale convolution module is proposed to extract fine-grain local structures. And a kind of multi-resolution residual fusion module is proposed to progressively reconstruct intermediate haze-free images. We have compared our method with several popular state-of-the-art methods on public RESIDE and 2018 NTIRE Dehazing datasets. The experiment results demonstrate that our method could restore satisfactory high-frequency textures and high-fidelity colors. Related source code and parameters will be distributed on Github for further study. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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