Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery

ZHANG Shihui; LU Jiaqi; SONG Dandan; ZHANG Xiaowei

doi:10.11999/JEIT210868

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3967-3976

ZHANG Shihui, LU Jiaqi, SONG Dandan, ZHANG Xiaowei. Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868

Citation:

ZHANG Shihui, LU Jiaqi, SONG Dandan, ZHANG Xiaowei. Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868

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Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery

doi: 10.11999/JEIT210868

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China

Funds: The Central Government Guided Local Funds for Science and Technology Development (216Z0301G), The Natural Science Foundation of Hebei Province (F2019203285)

Received Date: 2021-08-23
Accepted Date: 2021-12-14
Rev Recd Date: 2021-12-13

Available Online: 2021-12-27

Publish Date: 2022-11-14

Abstract

Abstract

In order to improve the accuracy of the single image dehazing method and the detail visibility of its dehazing results, a single image dehazing method based on multi-scale features combined with detail recovery is proposed. Firstly, according to the distribution characteristics and imaging principles of haze in images, the multi-scale feature extraction module and the multi-scale feature fusion module are designed to extract effectively the haze-related multi-scale features in the hazy image and perform nonlinear weighted fusion. Secondly, the end-to-end dehazing network based on the designed multi-scale feature extraction module and multi-scale feature fusion module are constructed, and the preliminary dehazing results are obtained by using this network. Then, a detail recovery network based on image blocking is constructed to extract detail information. Finally, the detail information extracted from the detail recovery network is fused with the preliminary dehazing results obtained from the dehazing network to obtain the final clear dehazed image, which can enhance the visual effect of the dehazing images. The experimental results show that compared with the existing representative image dehazing methods, the proposed method can effectively remove the haze in the synthetic images and the real-world images, and the detailed information of the dehazing results is kept.
- Image dehazing,
- Detail recover,
- Multi-scale features,
- Non-linear weighted fusion

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References(25)

References

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