Image Dehazing Based on the Optimum of UAV Aerial Image Quality Evaluation

doi:10.3969/j.issn.1000-1093.2022.01.016

Abstract

Abstract: UAV is an important means of information reconnaissance in modern warfare. The fog seriously affects the quality of UAV aerial images and hinders the ability to perform reconnaissance and recognition. In order to improve the effective utilization of UAV aerial images in the foggy environment，an image dehazing method based on the optimum of UAV aerial image quality evaluation is proposed according to the characteristics and special application background of UAV aerial images.A comprehensive image quality evaluation function is established by combining three image quality evaluation parameters，such as information entropy，standard deviation and image Fourier amplitude. Different atmospheric transmittance values are given，the corresponding dehazing image is solved，and the quality evaluation function value of dehazing image is used as the screening standard. The experimental verification shows that the image dehazing method based on the optimum of UAV aerial image quality evaluation has low computational complexity and simple operation. And the obtained dehazing image has clear visual effects and small result errors.

Key words: UAVaerialimage, imageprocessing, imagedehazing, qualityevaluation, imageFourieramplitude

CLC Number:

V279⁺.2

JIANG Yutong， SONG Haiping， WANG Guanghui. Image Dehazing Based on the Optimum of UAV Aerial Image Quality Evaluation[J]. Acta Armamentarii, 2022, 43(1): 148-158.

References

［1］杨棕源，唐友喜.无人机监测图像独立分量分析增强关键技术[D].成都：电子科技大学，2012.
YANG Z Y，TANG Y X.Key technologies for independent component analysis and enhancement of UAV monitoring images[D].Chengdu:University of Electronic Science and Technology of China，2012.(in Chinese)
[2] 孔庆红.基于暗原色先验的无人机航拍图像去雾算法研究与实现[D].青岛：中国海洋大学，2014.
KONG Q H.The defog algorithm on research and implementation of UAV aerial images based on dark channel prior[D].Qingdao:Ocean University of China，2014.(in Chinese)
[3] 黄宇晴，丁文锐，李红光.基于图像增强的无人机侦察图像去雾方法[J].北京航空航天大学学报，2017，43(3): 592-601.
HUANG Y Q，DING W R，LI H G. Haze removal method for UAV reconnaissance images based on image enhancement[J].Journal of Beijing University of Aeronautics and Astronautics，2017，43(3):592-601.(in Chinese)
[4] 刘春辉，齐越，丁文锐.基于大气光鲁棒估计的无人机图像去雾方法[J].北京航空航天大学学报，2017，43(6): 1105-1111.
LIU C H，QI Y，DING W R.A haze remove method for unmanned aerial vehicle images defogging method based on robust estimation of atmospheric light[J].Journal of Beijing University of Aeronautics and Astronautics，2017，43(6): 1105-1111.(in Chinese)
[5] 李春明，姜雨彤，宋海平，等.基于光学厚度代理模型的雾浓度估计及图像去雾[J].兵工学报，2019，40(7):1425-1433.
LI C M，JIANG Y T，SONG H P，et al.Research on optical depth surrogate model-based method for estimating fog density and removing fog effect from images[J].Acta Armamentarii，2019，40(7): 1425-1433.(in Chinese)
[6] 李力，金伟其，黄有为，等.一种低对比度可见光超视距成像的实时图像增强方法[J].兵工学报，2010，31(2): 242-247.
LI L，JIN W Q，HUANG Y W，et al. Real-time image enhancement processing methods of low contrast visible light over-the-horizon imaging[J].Acta Armamentarii，2010，31(2): 242-247. (in Chinese)
[7] ANCUTI， CODRUTA O，COSMIN A.Single image dehazing by multi-scale fusion[J].IEEE Transactions on Image Processing，2013，22(8): 3271-3282.
[8] 李从利，张思雨，韦哲，等.基于深度卷积生成对抗网络的航拍图像去厚云方法[J].兵工学报，2019，40(7):1434-1442.
LI C L，ZHANG S Y，WEI Z，et al. Thick cloud removal for aerial images based on deep convolutional generative adversarial networks[J].Acta Armamentarii，2019，40(7):1434-1442.(in Chinese)
[9] ENGIN D, GENA, EKENEL H K.Cycle-Dehaze:enhanced CycleGAN for single image dehazing[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City,UT,US:IEEE,2018.
[10] 李成超，于占江，李一全，等.微小零件显微检测图像的清晰度评价[J].半导体光电，2020，41(1):103-107，113.
LI C C，YU Z J，LI Y Q，et al.Sharpness evaluation of microsco- pic detection image for micro parts[J].Semiconductor Optoelectronics，2020，41(1):103-107，113.(in Chinese)
[11] HAJGHASSEM H，ZARIE M，POURMOHAMMAD A.Image contrast enhancement using triple clipped dynamic histogram equalization based on standard deviation[J].IET Image Processing，2019，13(7):1081-1089.
[12] KIM J H，JANG W D，SIM J Y，et al.Optimized contrast enhancement for real-time image and video dehazing[J].Journal of Visual Communication and Image Representation，2013，24(3): 410- 425.
[13] HE K M，SUN J，TANG X O.Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2011，33(12):2341-2353.
[14] ZHU Q S，MAI J M，SHAO L.A fast single image haze removal algorithm using color attenuation prior[J].IEEE Transactions on Image Processing，2015，24(11):3522-3533.
[15] WANG Z，BOVIK A C，SHEIKH H R，et al. Image quality assessment: from error visibility to structural similarity[J].IEEE Transactions on Image Processing，2004，13(4): 600-612.
[16] MATKOVIAC＇U2 K，NEUMANN L，NEUMANN A，et al.Global contrast factor - a new approach to image contrast[C]∥Proceedings of the 1st Eurographics Conference on Computational Aesthetics in Graphics，Visualization and Imaging.Girona，Spain:Institució Catalana de Recercai Estudis Avanats，2005:159-167.

[1]	YANG Jianxin， LAN Xiaoping， FENG Yadong， YANG Yiming， GUO Zhiming. An Ammunition Quality Evaluation Method Based on Least Squares Support Vector Machine [J]. Acta Armamentarii, 2022, 43(5): 1012-1022.
[2]	SONG Yu, WANG Yalin, DU Bojun, WANG Jun, DONG Xingfa. Detection of Overlapped Bullet Holes Based on Improved Otsu's Thresholding Method [J]. Acta Armamentarii, 2022, 43(4): 924-930.
[3]	QIN Ruolin， JIANG Xiaogang， JIN Liang'an， GAO Kexin. Study of Characteristics Extraction of Underwater Bubbles Group Based on Hough Transform [J]. Acta Armamentarii, 2019, 40(12): 2504-2512.