Defogging Image Optimization for Forest Background
-
摘要: 森林雾天常导致采集的图像信息质量差,为得到更为清晰完整的森林去雾图像,从而为森林监测提供更好的数据支持和保障,该文利用自适应性算法初步确定森林雾气含量以及光线强度等环境状况,再用亮暗通道融合算法对全局大气光、透射率等大气模型的重要参数值进行优化,最后针对亮暗通道先验算法造成的晕轮效应进行高斯曲率滤波处理。基于自适应图像增强的亮暗通道融合去雾算法能有效改善森林图像的质量,得到细节丰富、视觉效果清晰的森林去雾图像。实验分别从主观标准和客观标准对图像效果进行科学性评价,所得到的融合改进算法对森林图像优化有良好的效果。Abstract: Forest fog often leads to poor quality of collceted image information. In order to obtain clearer and complete images of forest defogging images and provide better data support and guarantee for forest monitoring, adaptive algorithm is used to preliminarily determine the forest fog content, light intensity and other environmental conditions. Then, the light and dark channel fusion algorithm is used to optimize the global atmospheric light, transimittance and other important parameters of the atmospheric model. Finally,Gaussian curvature filtering is used to deal with the halo effect caused by the light and dark channel prior algorithm. Light and dark channel fusion defogging algorithm based on adaptive image enhancement can effectively improve the quality of forest images, and obtain clear and complete forest defogging images with rich details and clear visual effects. The experiment evaluates the image effect scientifically from subjective standard and objective standard respetively, and the fusion improved algorithm obtained has good effect on forest image optimization.
-
-
[1] NARASIMHAN S G,NAYAR S K.Vision and the atmosphere[J].International Journal of Computer Vision,2002,48(3):233-254.
[2] 孙丽萍,卢俊彤,张怡卓,等.森林雾天单幅图像复原的研究[J].计算机仿真,2018,35(4):390-394. SUN L P,LU J T,ZHANG Y Z,et al.Research on restoring single foggy forest image[J].Computer Simulation,2018,35(4):390-394.
[3] 崔尚勇.改进量子遗传算法的图像增强研究[J].自动化技术与应用,2020,39(7):96-100. CUI S Y.Image enhancement research based on improved quantum genetic algorithm[J].Techniques of Automation and Applications,2020,39(7):96-100.
[4] 唐敏,刘英,费叶琦,等.图像处理技术在现代林果采摘中的应用[J].林业机械与木工设备,2020,48(4):4-7. TANG M,LIU Y,FEI Y Q,et al.Application of image processing technology in modern forest fruit picking[J].Forestry Machinery&Woodworking Equipment,2020,48(4):4-7.
[5] LIANG W,LONG J,LI K C,et al.A fast defogging image recognition algorithm based on bilateral hybrid filtering[J].ACM Transactions on Multimedia Computing,Communications,and Applications,2021,17(2):1-16.
[6] ABRAHAMS A,ORAM C,LOZANO-GRACIA N.Deblurring DM-SP nighttime lights:a new method using Gaussian filters and frequencies of illumination[J].Remote Sensing of Environment,2018,210:242-258.
[7] 郑鑫.通过混浊大气介质的成像质量研究[D].合肥:中国科学技术大学,2019. ZHENG X.Study on imaging quality through turbid atmosphere[D].Hefei:University of Science and Technology of China,2019.
[8] LI C L,FAN T H,MA X,et al.An improved image defogging method based on dark channel prior[C]//2017 2nd International Conference on Image,Vision and Computing (ICIVC).June 2-4,2017,Chengdu.IEEE,2017:414-417.
[9] YAN Y Y,REN W Q,GUO Y F,et al.Image deblurring via extreme channels prior[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition.July 21-26,2017,Honolulu,HI,USA.IEEE,2017:6978-6986.
[10] 李鹏飞,何小海,卿粼波,等.暗通道融合亮通道优化的夜间图像去雾算法[J].液晶与显示,2021,36(4):596-604. LI P F,HE X H,QING L B,et al.Nighttime dehazing algorithm of dark channel and bright channel fusion optimization[J].Chinese Journal of Liquid Crystals and Displays,2021,36(4):596-604.
[11] 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.
[12] 蒯峰阳,张丹.基于亮暗通道相结合的自适应图像去雾算法[J].计算技术与自动化,2021,40(2):118-124. KUAI F Y,ZHANG D.Adaptive single image haze removal using integrated dark and bright channel prior[J].Computing Technology and Automation,2021,40(2):118-124.
[13] 赵伟,张南楠.恶劣雾霾天气车牌图像增强算法仿真[J].计算机仿真,2019,36(3):207-211. ZHAO W,ZHANG N N.Simulation of license plate image enhancement algorithm in severe hazy weather[J].Computer Simulation,2019,36(3):207-211.
[14] 庄秀玲,谭福奎,李震,等.基于暗通道先验和优化自动色阶的图像去雾算法[J].计算机应用与软件,2021,38(7):190-195. ZHUANG X L,TAN F K,LI Z,et al.Image defogging algorithm based on dark channel prior and optimized auto-color[J].Computer Applications and Software,2021,38(7):190-195.
[15] AHN H,KEUM B,KIM D,et al.Adaptive local tone mapping based on retinex for high dynamic range images[C]//2013 IEEEInternational Conference on Consumer Electronics.January 11-14,2013,Las Vegas,NV,USA.IEEE,2013:153-156.
[16] LEE S H,SEO J K.Noise removal with Gauss curvature-driven diffusion[J].IEEE Transactions on Image Processing,2005,14(7):904-909.
[17] GONG Y H.Spectrally regularized surfaces[D].Switzerland:ETH Zurich,2015.
[18] 林朝剑,张广群,杨洁,等.基于迁移学习的林业业务图像识别[J].南京林业大学学报(自然科学版),2020,44(4):215-221. LIN C J,ZHANG G Q,YANG J,et al.Transfer learning based recognition for forestry business images[J].Journal of Nanjing Forestry University(Natural Science Edition),2020,44(4):215-221.
[19] 陈维桓.微分几何初步[M].北京:北京大学出版社,1990. CHEN W H.Preliminary of differential geometry[M].Bejing:Peking University Press,1990.
[20] 杨珂珂,贾渊,沈川.结合曲率滤波的HTM算法去除遥感影像云雾[J].中国图象图形学报,2020,25(4):791-800. YANG K K,JIA Y,SHEN C.Haze and cloud removal from remote sensing image using HTM algorithm based on curvature filtering[J].Journal of Image and Graphics,2020,25(4):791-800.
-
期刊类型引用(3)
1. 周思琦，管志光，殷珊珊，郭子屹，林明星. 基于WLS滤波优化的双目视觉深度图重建. 山东交通学院学报. 2024(02): 139-145 . 
百度学术
2. 李云飞，蒙宇，周凯. 基于图像处理技术的人机交互界面控制研究. 自动化技术与应用. 2023(01): 17-20+29 . 百度学术
3. 陈文继. 基于Radon变换的健美操跳跃动作轨迹实时提取方法. 自动化技术与应用. 2023(01): 60-63+81 . 百度学术
其他类型引用(4)
计量
- 文章访问数: 3
- HTML全文浏览量: 0
- PDF下载量: 0
- 被引次数: 7
下载:
