Mask information-based gamma correction in fringe projection profilometry

被引:9
|
作者
Song, Huixin [1 ]
Kong, Lingbao [1 ]
机构
[1] Fudan Univ, Sch Informat Sci & Technol, Shanghai Engn Res Ctr Ultraprecis Opt Mfg, Shanghai 200433, Peoples R China
基金
中国国家自然科学基金;
关键词
PHASE; ALGORITHMS; COMPENSATION; NONLINEARITY; SURFACES;
D O I
10.1364/OE.492176
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
For fringe projection profilometry (FPP), the gamma effect of the camera and projector will cause non-sinusoidal distortion of the fringe patterns, leading to periodic phase errors and ultimately affecting the reconstruction accuracy. This paper presents a gamma correction method based on mask information. Since the gamma effect will introduce higher-order harmonics into the fringe patterns, on top of projecting two sequences of phase-shifting fringe patterns having different frequencies, a mask image is projected to provide enough information to determine the coefficients of higher-order fringe harmonics using the least-squares method. The true phase is then calculated using Gaussian Newton iteration to compensate for the phase error due to the gamma effect. It does not require projecting a large number of images, and only 2 x 3 phase shift patterns and 1 mask pattern minimum are required. Simulation and experimental results demonstrate that the method can effectively correct the errors caused by the gamma effect. & COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:19478 / 19490
页数:13
相关论文
共 50 条
  • [41] High-speed fringe projection profilometry based on convolutional neural network
    Wang, Jiaye
    Zhang, Yuzhen
    AOPC 2021: INFRARED DEVICE AND INFRARED TECHNOLOGY, 2021, 12061
  • [42] Determination of optimal binary defocusing based on digital correlation for fringe projection profilometry
    Kang, Xin
    Yin, Zhuoyi
    Dong, Shuai
    He, Xiaoyuan
    OPTIK, 2023, 272
  • [43] Single fringe projection profilometry based on sinusoidal intensity normalization and subpixel fitting
    Yang Fujun
    Dai Meiling
    He Xiaoyuan
    Du Xiaolei
    OPTICS AND LASERS IN ENGINEERING, 2011, 49 (03) : 465 - 472
  • [44] Untrained deep learning-based phase retrieval for fringe projection profilometry
    Yu, Haotian
    Chen, Xiaoyu
    Huang, Ruobing
    Bai, Lianfa
    Zheng, Dongliang
    Han, Jing
    OPTICS AND LASERS IN ENGINEERING, 2023, 164
  • [45] Harmonics suppression in frequency domain for fringe projection profilometry with arbitrary phase shifts
    Lin, Shuai
    Zhu, Jianli
    Guo, Hongwei
    OPTICS COMMUNICATIONS, 2025, 576
  • [46] Evaluating binary defocusing quantitatively in real-time for fringe projection profilometry
    Kang, Xin
    Yin, Zhuoyi
    Dong, Shuai
    Liu, Cong
    He, Xiaoyuan
    OPTICAL ENGINEERING, 2021, 60 (06)
  • [47] Sign language learning based on high-speed fringe projection profilometry employing defocused binary fringe
    Wang, Jian-hua
    Zhou, Yu-guo
    Yang, Yan-xi
    OPTOELECTRONICS LETTERS, 2020, 16 (01) : 65 - 74
  • [48] Binocular fringe projection profilometry for the metrology of meter-scale optical surfaces
    Berkson, Joel
    Hyatt, Justin
    Kang, Hyukmo
    Ordones, Sotero
    Quach, Henry
    Kim, Daewook
    OPTICS CONTINUUM, 2023, 2 (04): : 697 - 711
  • [49] Fringe projection profilometry applications: preliminary results for measurements of a swordfish
    Nava-Vega, A.
    Serrano-Trujillo, A.
    Salinas-Luna, J.
    OPTICAL MEASUREMENT TECHNIQUES FOR STRUCTURES & SYSTEMS III, 2016, : 225 - 231
  • [50] Temporal fringe projection profilometry: Modified fringe-frequency range for error reduction
    Burnes, Susana
    Villa, Jesus
    Moreno, Gamaliel
    de la Rosa, Ismael
    Alaniz, Daniel
    Gonzalez, Efren
    OPTICS AND LASERS IN ENGINEERING, 2022, 149