Volume 16 Issue 3
May  2023
Turn off MathJax
Article Contents
JIE Deng-fei, WANG Hao, LV Hui-fang, TIAN Bo-tao, ZHANG Zhan-xiang. An improved algorithm for monocular camera edge spectrum based ranging by defocused images[J]. Chinese Optics, 2023, 16(3): 627-636. doi: 10.37188/CO.2022-0171
Citation: JIE Deng-fei, WANG Hao, LV Hui-fang, TIAN Bo-tao, ZHANG Zhan-xiang. An improved algorithm for monocular camera edge spectrum based ranging by defocused images[J]. Chinese Optics, 2023, 16(3): 627-636. doi: 10.37188/CO.2022-0171

An improved algorithm for monocular camera edge spectrum based ranging by defocused images

doi: 10.37188/CO.2022-0171
Funds:  Supported by Fujian Provincial Natural Science Foundation Project (No. 2020J01577); the Fujian Key Laboratory of Agricultural Information Sensoring Technology (No. 2021ZDSYS0101)
More Information
  • Corresponding author: jiedengfei@163.com
  • Received Date: 23 Jul 2022
  • Rev Recd Date: 06 Sep 2022
  • Accepted Date: 24 Dec 2022
  • Available Online: 24 Dec 2022
  • In order to achieve accurate target ranging of weak or non surface texture features using a monocular camera, an improved defocused image ranging algorithm based on preserving edge spectral information is presented. By comparing two classical defocal ranging theories with Fourier transform and Laplace transform as the foundational principals of calculation, a corresponding definition evaluation function is constructed. We select the method based on the spectrum definition function with better sensitivity, and select the calculation range of the frequency domain by retaining the information on the target edge. To verify the feasibility of the algorithm, 6 sets of different duck egg samples are used to obtain scattered focus images of different apertures and distances, and the improved algorithm was used to solve the distance of the duck eggs from the camera lens. The experimental results show that the improved algorithm based on the edge spectrum preservation has a good ranging effect with a correlation coefficient of 0.986 and Root Mean Square Error (RMSE) of 11.39 mm. It is found that the range ability can be effectively improved after the image rotation processing of the duck egg image taken at an oblique angle, with the RMSE is reduced from 11.39 mm to 8.76 mm, the average relative error is reduced from 2.85% to 2.28% and the correlation coefficient reaches 0.99. The proposed algorithm fundamentally meets the requirements of stability and high accuracy in ranging targets with weak or non surface texture features.

     

  • loading
  • [1]
    GONGAL A, AMATYA S, KARKEE M, et al. Sensors and systems for fruit detection and localization: a review[J]. Computers and Electronics in Agriculture, 2015, 116: 8-19. doi: 10.1016/j.compag.2015.05.021
    [2]
    蔡明兵, 刘晶红, 徐芳. 无人机侦察多目标实时定位技术研究[J]. 中国光学,2018,11(5):812-821. doi: 10.3788/co.20181105.0812

    CAI M B, LIU J H, XU F. Multi-targets real-time location technology for UAV reconnaissance[J]. Chinese Optics, 2018, 11(5): 812-821. (in Chinese) doi: 10.3788/co.20181105.0812
    [3]
    MIRHAJI H, SOLEYMANI M, ASAKEREH A, et al. Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions[J]. Computers and Electronics in Agriculture, 2021, 191: 106533. doi: 10.1016/j.compag.2021.106533
    [4]
    张石磊, 崔宇, 邢慕增, 等. 光场成像目标测距技术[J]. 中国光学,2020,13(6):1332-1342. doi: 10.37188/CO.2020-0043

    ZHANG SH L, CUI Y, XING M Z, et al. Light field imaging target ranging technology[J]. Chinese Optics, 2020, 13(6): 1332-1342. (in Chinese) doi: 10.37188/CO.2020-0043
    [5]
    熊锐. 基于数字图像处理的显微自动对焦技术研究[D]. 成都: 中国科学院大学(中国科学院光电技术研究所), 2021.

    XIONG R. Study on microscopic autofocus technology based on digital image processing[D]. Chengdu: The Institute of Optics and Electronics, The Chinese Academy of Sciences, 2021. (in Chinese)
    [6]
    李喆, 李建增, 胡永江, 等. 基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法[J]. 图学学报,2018,39(5):909-916.

    LI ZH, LI J Z, HU Y J, et al. Blind restoration of focus blur based on spectrum preprocessing and improved Hough transform[J]. Journal of Graphics, 2018, 39(5): 909-916. (in Chinese)
    [7]
    RASHWAN H A, CHAMBON S, GURDJOS P, et al. Using curvilinear features in focus for registering a single image to a 3D object[J]. IEEE Transactions on Image Processing, 2019, 28(9): 4429-4443. doi: 10.1109/TIP.2019.2911484
    [8]
    SUBBARAO M. Direct recovery of depth-map I: differential methods[C]. Proceedings of the IEEE Computer Society Workshop on Computer Vision, IEEE, 1987: 58-65.
    [9]
    SUBBARAO M, SURYA G. Depth from defocus: a spatial domain approach[J]. International Journal of Computer Vision, 1994, 13(3): 271-294. doi: 10.1007/BF02028349
    [10]
    RAJAGOPALAN A N, CHAUDHURI S, MUDENAGUDI U. Depth estimation and image restoration using defocused stereo pairs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1521-1525. doi: 10.1109/TPAMI.2004.102
    [11]
    王海娟. 基于散焦图像的深度估计的研究[D]. 青岛: 中国海洋大学, 2011.

    WANG H J. Study on the estimate of depth based on defocus image[D]. Qingdao: Ocean University of China, 2011. (in Chinese)
    [12]
    马艳娥. 基于散焦图像测距的目标尺寸测量技术研究[D]. 太原: 中北大学, 2012.

    MA Y E. Research of objective measuring technique based on image distance measurement by defocusing[D]. Taiyuan: North University of China, 2012. (in Chinese)
    [13]
    薛松, 王文剑. 基于超像素分割的单幅散焦图像深度恢复方法[J]. 计算机科学与探索,2018,12(7):1162-1168. doi: 10.3778/j.issn.1673-9418.1705042

    XUE S, WANG W J. Depth estimation from single defocused image based on superpixel segmentation[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(7): 1162-1168. (in Chinese) doi: 10.3778/j.issn.1673-9418.1705042
    [14]
    薛松, 王文剑. 基于高斯-柯西混合模型的单幅散焦图像深度恢复方法[J]. 计算机科学,2017,44(1):32-36. doi: 10.11896/j.issn.1002-137X.2017.01.006

    XUE S, WANG W J. Depth estimation from single defocused image based on Gaussian-Cauchy mixed model[J]. Computer Science, 2017, 44(1): 32-36. (in Chinese) doi: 10.11896/j.issn.1002-137X.2017.01.006
    [15]
    韩丽燕, 王黎明, 刘宾. 一种基于边缘扩散函数描述散焦程度的测距算法[J]. 传感器世界,2011,17(2):9-11. doi: 10.3969/j.issn.1006-883X.2011.02.002

    HAN L Y, WANG L M, LIU B. Depth measuring method based on using edge spread function to describe defocusing degrees of images[J]. Sensor World, 2011, 17(2): 9-11. (in Chinese) doi: 10.3969/j.issn.1006-883X.2011.02.002
    [16]
    袁红星, 吴少群, 安鹏, 等. 对象引导的单幅散焦图像深度提取方法[J]. 电子学报,2014,42(10):2009-2015. doi: 10.3969/j.issn.0372-2112.2014.10.022

    YUAN H X, WU SH Q, AN P, et al. Object guided depth map recovery from a single defocused image[J]. Acta Electronica Sinica, 2014, 42(10): 2009-2015. (in Chinese) doi: 10.3969/j.issn.0372-2112.2014.10.022
    [17]
    ERKAN U, ENGINOĞLU S, THANH D N H, et al. Adaptive frequency median filter for the salt and pepper denoising problem[J]. IET Image Processing, 2020, 14(7): 1291-1302. doi: 10.1049/iet-ipr.2019.0398
    [18]
    刘聪, 董文飞, 蒋克明, 等. 基于改进分水岭分割算法的致密荧光微滴识别[J]. 中国光学,2019,12(4):783-790. doi: 10.3788/co.20191204.0783

    LIU C, DONG W F, JIANG K M, et al. Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm[J]. Chinese Optics, 2019, 12(4): 783-790. (in Chinese) doi: 10.3788/co.20191204.0783
    [19]
    PENTLAND A, SCHEROCK S, DARRELL T, et al. Simple range cameras based on focal error[J]. Journal of the Optical Society of America A, 1994, 11(11): 2925-2934. doi: 10.1364/JOSAA.11.002925
    [20]
    葛鹏, 游耀堂. 基于稀疏表示的光场图像超分辨率重建[J]. 激光与光电子学进展,2022,59(2):0210001.

    GE P, YOU Y T. Super-resolution reconstruction of light field images via sparse representation[J]. Laser &Optoelectronics Progress, 2022, 59(2): 0210001. (in Chinese)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)  / Tables(4)

    Article views(349) PDF downloads(223) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return