融合MCAP和GRTV正则化的无人机航拍建筑物图像去雾方法

doi:10.16183/j.cnki.jsjtu.2021.238

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (3): 366-378.doi: 10.16183/j.cnki.jsjtu.2021.238

所属专题：《上海交通大学学报》2023年“电子信息与电气工程”专题

• 电子信息与电气工程 • 上一篇

融合MCAP和GRTV正则化的无人机航拍建筑物图像去雾方法

黄鹤^a^,^b, 胡凯益^a^,^b, 李战一^a^,^b, 王会峰^a^,^b, 茹锋^a^,^b, 王珺^a()

a.长安大学西安市智慧高速公路信息融合与控制重点实验室,西安 710064
b.长安大学电子与控制工程学院,西安 710064

收稿日期:2021-06-20 接受日期:2021-08-01 出版日期:2023-03-28 发布日期:2023-03-30
通讯作者: 王珺,副教授,电话(Tel.): 029-88308121; E-mail:jwang@nwu.edu.cn.
作者简介:黄鹤(1979-),教授,主要从事信息融合,图像处理等方向研究.
基金资助:
国家重点研发计划项目(2021YFB2501200);国家自然科学基金面上项目(52172324);国家自然科学基金面上项目(52172379);陕西省重点研发计划项目(2021GY-285);陕西省自然科学基础研究计划面上项目(2021JM-184);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金项目(300102321502)

An Image Dehazing Method for UAV Aerial Photography of Buildings Combining MCAP and GRTV Regularization

HUANG He^a^,^b, HU Kaiyi^a^,^b, LI Zhanyi^a^,^b, WANG Huifeng^a^,^b, RU Feng^a^,^b, WANG Jun^a()

a. Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control
b. School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

Received:2021-06-20 Accepted:2021-08-01 Online:2023-03-28 Published:2023-03-30

摘要/Abstract

摘要：

针对传统去雾处理复原得到的图像清晰度和对比度较低、整体颜色偏暗的问题,提出了一种改进的图像去雾方法,应用于无人机航拍建筑物图像处理中.针对全局大气光取值易受场景中景物影响的问题,提出一种颜色衰减先验投影最小方差的大气光求解方法,构建明度与饱和度差值图像,求解最小方差出现区域,并确定全局大气光估计.将利用图像场景深度信息求解的区域大气光与全局大气光相融合,获得新的大气光图.采用基于非局部信息的雾霾线先验理论对透射率进行优化,提出了一种基于雾霾线理论和引导相对总变分正则化的算法,通过计算透射率可靠性函数对透射率修正,并消除图像中存在的大量无用纹理信息,提升了透射率估计精度,有效改善了无人机航拍场景中浓雾及景深突变区域的复原图像质量.实验结果表明,所提算法与其他算法相比,获得的复原图像平均梯度、对比度、雾霾感知密度估计及模糊系数等指标分别平均提升了12.2%、7.0%、11.9%和12.5%,运算时长也优于部分算法,航拍图像更加清晰,更符合人眼视觉感受.

关键词: 颜色衰减先验, 图像处理, 变差函数, 去雾, 无人机

Abstract:

Aimed at the problems of low resolution, low contrast,and dark color of images recovered by traditional dehazing processing, an improved images dehazing method is proposed and applied to the unmanned aerial vehicle (UAV) aerial building image processing. First, to solve the problem that the value of global atmospheric light is easily affected by the scene objects, a method of atmospheric light with minimum variance of color attenuation prior projection is proposed. The difference image of brightness and saturation is constructed to solve the region where the minimum variance occurres, and the estimation of global atmospheric light is determined. Then, the regional atmospheric light is fused with the global atmospheric light by using the depth information of the image scene, and a new atmospheric light image is obtained. Finally, the haze line based on the non-local information prior theory in view of the transmittance is optimized. Moreover, this paper proposes a method based on the theory of haze line and guide relative to the total variation regularization algorithm. The transmission rate is fixed through calculating transmittance reliability function. A large amount of useless texture information existing in the image is eliminated, which enhances the precision of transmission rate estimation. It effectively improves the image quality of thick haze and abrupt depth-of-field in UAV aerial shooting scene. The experimental results show that, compared with other algorithms, the average gradient, contrast, haze aware density evaluator, and blur coefficient of the recovered images are improved by 12.2%, 7.0%, 11.9%, and 12.5%, respectively. The operation time of the proposed algorithm is shorter than that of some other algorithms, and the processed aerial images are clearer, which are more consistent with the visual perception of human eyes.

Key words: color attenuation prior, image processing, variation function, dehazing, unmanned aerial vehicle (UAV)

中图分类号:

TP391.41

黄鹤, 胡凯益, 李战一, 王会峰, 茹锋, 王珺. 融合MCAP和GRTV正则化的无人机航拍建筑物图像去雾方法[J]. 上海交通大学学报, 2023, 57(3): 366-378.

HUANG He, HU Kaiyi, LI Zhanyi, WANG Huifeng, RU Feng, WANG Jun. An Image Dehazing Method for UAV Aerial Photography of Buildings Combining MCAP and GRTV Regularization[J]. Journal of Shanghai Jiao Tong University, 2023, 57(3): 366-378.

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参考文献 17

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实验组	去雾算法	平均梯度	灰度图像对比度	FADE	模糊系数	运算时长/s
E1	原始图像	3.4132	38.0438	3.3746	—	—
	DCP^[5]	4.6308	41.5066	1.6240	1.2875	10.83
	FVID^[13]	5.0402	44.8432	1.5107	1.3973	1.22
	Haze-Line^[8]	7.1235	66.1660	0.9111	1.9979	5.34
	DehazeNet^[7]	5.0708	55.1841	1.5434	1.3558	1.27
	IGSK^[14]	2.9055	43.6854	1.8265	0.4058	16.89
	MSW^[15]	5.4143	53.9913	1.4022	1.4407	2.54
	本文方法	8.1420	72.9530	0.7171	2.3017	6.21
E2	原始图像	2.7286	49.5167	2.3164	—	—
	DCP^[5]	3.6947	56.4930	1.0717	1.3913	10.81
	FVID^[13]	4.4220	53.6060	1.2449	1.6863	0.81
	Haze-Line^[8]	4.9044	58.5268	0.5796	1.8093	4.07
	DehazeNet^[7]	4.1678	67.0087	0.7425	1.5658	1.27
	IGSK^[14]	2.0462	49.3389	2.9110	0.6316	16.40
	MSW^[15]	4.4642	71.8793	0.7974	1.6494	1.56
	本文方法	6.8819	72.3219	0.3560	2.5359	5.00
E3	原始图像	4.0277	34.3381	1.4294	—	—
	DCP^[5]	6.2019	37.0552	0.5512	1.5606	10.78
	FVID^[13]	6.5895	36.3347	0.5775	1.7264	0.81
	Haze-Line^[8]	8.5561	34.4546	0.2612	2.1607	4.68
	DehazeNet^[7]	6.4785	44.1485	0.4464	1.6495	1.25
	IGSK^[14]	3.0247	33.7982	1.8486	0.6427	16.47
	MSW^[15]	7.3224	50.1062	0.4932	1.8492	1.54
	本文方法	11.6165	57.2106	0.1953	2.9986	5.01
E4	原始图像	3.6964	28.8263	2.2842	—	—
	DCP^[5]	5.1626	35.8074	1.1885	1.6117	10.89
	FVID^[13]	5.7754	19.5894	1.2384	1.7113	0.81
	Haze-Line^[8]	9.7989	57.8329	0.4250	2.5171	4.34
	DehazeNet^[7]	5.3420	38.9765	1.3434	1.6479	1.31
	IGSK^[14]	1.8620	28.6709	3.4998	0.2421	16.58
	MSW^[15]	6.2033	48.6046	1.0322	1.7546	1.53
	本文方法	9.6690	59.4137	0.2788	2.5483	4.72

融合MCAP和GRTV正则化的无人机航拍建筑物图像去雾方法

An Image Dehazing Method for UAV Aerial Photography of Buildings Combining MCAP and GRTV Regularization

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