Underwater Image Restoration Based on Background Light Corrected Image Formation Model

ZHOU Yan; GU Xintao; LI Qingwu

doi:10.11999/JEIT211012

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3363-3371

ZHOU Yan, GU Xintao, LI Qingwu. Underwater Image Restoration Based on Background Light Corrected Image Formation Model[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3363-3371. doi: 10.11999/JEIT211012

Citation:

ZHOU Yan, GU Xintao, LI Qingwu. Underwater Image Restoration Based on Background Light Corrected Image Formation Model[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3363-3371. doi: 10.11999/JEIT211012

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Underwater Image Restoration Based on Background Light Corrected Image Formation Model

doi: 10.11999/JEIT211012

ZHOU Yan^{1, 2
,
,},
GU Xintao¹,
LI Qingwu^{1, 2}

1.
College of IOT Engineering, Hohai University, Changzhou 213022, China
2.
Changzhou Key Laboratory of Sensor Networks and Environment Sensing, Changzhou 213022, China

Funds: The National Key R&D Program of China (2018YFC0406903), The National Natural Science Foundation of China (41706103), The Natural Science Foundation of Jiangsu Province (BK20170306)

Received Date: 2021-09-23
Accepted Date: 2022-03-22
Rev Recd Date: 2022-03-11

Available Online: 2022-03-24

Publish Date: 2022-10-19

Abstract

Abstract

The underwater image quality is seriously degraded due to the effects of absorption and scattering when light propagates underwater. In order to remove color distortion and blur, and improve the quality of underwater image effectively, an underwater image restoration method based on background light corrected image formation model is proposed in this paper. Based on the observation of ground hazy images, the assumption of background light offset for underwater images is put forward, which is the cornerstone of the background light corrected image formation model. Then, a monocular depth estimation network is used to obtain the estimate of the scene depth. Combined with the background light corrected image formation model, the underwater offset component is obtained by non-linear least square fitting, so as to remove water from underwater images. Finally, the transmittance of hazy image after water removed is optimized and combined with the corrected background light to achieve image recovery. Experimental results show that the method works well in restoring the original color of underwater scenes and removing scattered light.
- Underwater image,
- Color correction,
- Image restoration,
- Background light corrected image formation model,
- Transmittance optimization

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References(19)

References

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