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

doi:10.16183/j.cnki.jsjtu.2021.238

Abstract

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)

CLC Number:

TP391.41

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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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