基于背景光修正成像模型的水下图像复原

周妍; 顾鑫涛; 李庆武

doi:10.11999/JEIT211012

基于背景光修正成像模型的水下图像复原

doi: 10.11999/JEIT211012

周妍^{1, 2, ,},
顾鑫涛¹,
李庆武^{1, 2}

1.
河海大学物联网工程学院常州 213022
2.
常州市传感网与环境感知重点实验室常州 213022

基金项目: 国家重点研发计划(2018YFC0406903)，国家自然科学基金(41706103)，江苏省自然科学基金(BK20170306)

详细信息

作者简介:
周妍：女，副教授，研究方向为水下环境感知与图像处理

顾鑫涛：男，硕士生，研究方向为水下图像处理

李庆武：男，教授，研究方向为智能感知与信息处理

通讯作者:
周妍　yanzhou@hhu.edu.cn

中图分类号: TN911.73; TP391
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- 文章访问数: 914
- HTML全文浏览量: 206
- PDF下载量: 218
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-23
- 修回日期: 2022-03-11
- 录用日期: 2022-03-22
- 网络出版日期: 2022-03-24
- 刊出日期: 2022-10-19

Underwater Image Restoration Based on Background Light Corrected Image Formation Model

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)

摘要

摘要: 光在水下传播时由于受到水体吸收和散射作用的影响，导致水下图像质量严重退化。为了有效去除色偏和模糊，改善水下图像质量，该文提出一种基于背景光修正成像模型的水下图像复原方法。该方法基于对雾天图像的观察，提出了水下图像背景光偏移假设，并基于此建立背景光修正成像模型；随后使用单目深度估计网络获得场景深度的估计，并结合背景光修正的水下成像模型，利用非线性最小二乘拟合获得水下偏移分量的估计值从而实现水下图像去水；最后优化去水后的含雾图像的透射率，并结合修正后的背景光实现图像复原。实验结果表明，该文方法在恢复水下图像颜色和去除散射光方面效果良好。
- 水下图像 /
- 颜色校正 /
- 图像复原 /
- 背景光修正成像模型 /
- 透射率优化
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

HTML全文

图 1 自然含雾图像的大气光

下载: 全尺寸图片幻灯片

图 2 水下背景光偏移示意图

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图 3 雾天图像和水下图像不同场景深度处像素均值对比

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图 4 水下背景光偏移假设建模示意图

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图 5 每根雾线中清晰像素点的选取

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图 6 本文算法框架图

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图 7 不同水体中各算法复原结果与空气中原图对比

下载: 全尺寸图片幻灯片

图 8 水下降质图像复原对比

下载: 全尺寸图片幻灯片

表 1 不同水体中各算法复原结果的CIEDE2000色差指标对比

	文献[4]	文献[16]	文献[17]	本文算法
水体1	16.1403	23.6964	22.4461	16.8627
水体2	23.1050	28.8191	32.1677	22.4738
水体3	14.0537	16.1188	28.1616	11.9412
水体4	14.7398	16.2605	27.4461	12.2755

下载: 导出CSV

表 2 各方法客观评价指标平均值比较

方法	低质量水下图像				低照度水下图像
方法	AG	IE	UCIQE	UIQM	AG	IE	UCIQE	UIQM
原图	6.4231	2.1744	0.4030	3.5151	6.0967	1.3027	0.4421	2.3293
文献[16]	6.9084	4.7642	0.5957	4.3580	6.8364	3.7480	0.5592	2.5836
文献[17]	7.3189	5.1387	0.6098	4.7196	7.2517	3.9023	0.5806	3.0269
文献[18]	7.2730	5.0059	0.5737	4.6711	6.4905	2.6523	0.5755	2.6256
文献[19]	7.0860	5.0510	0.5847	4.4900	6.9061	3.4108	0.5766	2.9130
本文算法	7.3238	5.1482	0.6280	4.6647	7.5311	4.2754	0.5724	3.0785

下载: 导出CSV

参考文献(19)

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