Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

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...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 54514 - 54521
Main Authors Yi, Qiaosi, Jiang, Aiwen, Li, Juncheng, Wan, Jianyi, Wang, Mingwen
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Atmospheric modeling
Computer vision
Convolution
Crystal defects
Haze
haze removal
Image color analysis
Image dehaze
image enhancement
Image reconstruction
Image restoration
Kernel
Modules
multiscale fusion
Scattering
Source code
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2981491