Absolute Phase Recovery of Single Frame Composite Image Based on Convolutional Neural Network

Wenjian Li; Shaoyan Gai; Jian Yu; Feipeng Da

doi:10.3788/AOS202141.2312001

[1] Salvi J, Fernandez S, Pribanic T et al. A state of the art in structured light patterns for surface profilometry[J]. Pattern Recognition, 43, 2666-2680(2010).

[2] Song Z, Chung R. Determining both surface position and orientation in structured-light-based sensing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1770-1780(2010).

[3] Zuo C, Feng S J, Huang L et al. Phase shifting algorithms for fringe projection profilometry: a review[J]. Optics and Lasers in Engineering, 109, 23-59(2018).

[4] Su X Y, Zhang Q C. Dynamic 3-D shape measurement method: a review[J]. Optics and Lasers in Engineering, 48, 191-204(2010).

[5] Qian K M. Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications and implementations[J]. Optics and Lasers in Engineering, 45, 304-317(2007).

[6] Zuo C, Huang L, Zhang M L et al. Temporal phase unwrapping algorithms for fringe projection profilometry: a comparative review[J]. Optics and Lasers in Engineering, 85, 84-103(2016).

[7] Su X Y, Chen W J. Reliability-guided phase unwrapping algorithm: a review[J]. Optics and Lasers in Engineering, 42, 245-261(2004).

[8] Zheng D L, Da F P, Qian K M et al. Phase-shifting profilometry combined with gray-code patterns projection: unwrapping error removal by an adaptive median filter[J]. Optics Express, 25, 4700-4713(2017).

[9] Cheng Y Y, Wyant J C. Multiple-wavelength phase-shifting interferometry[J]. Applied Optics, 24, 804-807(1985).

[10] Da J, Qu H M, Tao T Y et al. Real-time three-dimensional measurement composite of epipolar constraint and speckle correlation[J]. Acta Optica Sinica, 36, 1012003(2016).

[11] Deng H X, Ling X, Wang Y Y et al. High-speed and high-accuracy fringe projection profilometry without phase unwrapping[J]. Optics and Lasers in Engineering, 140, 106518(2021).

[12] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

[13] Luo W J, Schwing A G, Urtasun R. Efficient deep learning for stereo matching[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 5695-5703(2016).

[14] Kuznietsov Y, Stückler J, Leibe B. Semi-supervised deep learning for monocular depth map prediction[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2215-2223(2017).

[15] Zhong J X, Yin W, Feng S J et al. Speckle projection profilometry with deep learning[J]. Infrared and Laser Engineering, 49, 20200011(2020).

[16] Feng S J, Chen Q, Gu G H et al. Fringe pattern analysis using deep learning[J]. Advanced Photonics, 1, 025001(2019).

[17] Qiao G, Huang Y Y, Song Y P et al. A single-shot phase retrieval method for phase measuring deflectometry based on deep learning[J]. Optics Communications, 476, 126303(2020).

[18] Wang K Q, Li Y, Qian K M et al. One-step robust deep learning phase unwrapping[J]. Optics Express, 27, 15100-15115(2019).

[19] Law W. Budianto,. Lun D P K. Deep learning based period order detection in structured light three-dimensional scanning[C]∥2019 IEEE International Symposium on Circuits and Systems (ISCAS), May 26-29, 2019, Sapporo, Japan., 18815870(2019).

[20] van der Jeught S, Dirckx J J J. Deep neural networks for single shot structured light profilometry[J]. Optics Express, 27, 17091-17101(2019).

[21] Zhang Y Y, Xiong Z W, Wu F. Unambiguous 3D measurement from speckle-embedded fringe[J]. Applied Optics, 52, 7797-7805(2013).

[22] Zhang Z, Han B W, Yu H T et al. Multi-stage deep learning based single-frame fringe projection 3D measurement method[J]. Infrared and Laser Engineering, 49, 20200023(2020).

[23] Hara K, Saito D, Shouno H. Analysis of function of rectified linear unit used in deep learning[C]∥2015 International Joint Conference on Neural Networks (IJCNN), July 12-17, 2015, Killarney, Ireland., 15503788(2015).

[24] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[25] Kingma D P. -12-22)[2021-04-10]. https:∥arxiv., org/abs/1412, 6980(2014).