Infrared and Laser Engineering, Volume. 51, Issue 2, 20220006(2022)

A learning based on approach for noise reduction with raster images

Jiaye Wang1...2,3, Yixuan Li1,2,3, and Yuzhen Zhang1,23 |Show fewer author(s)
Author Affiliations
  • 1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • 2Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
  • 3Smart Computational Imaging Laboratory (SCILab), Nanjing University of Science and Technology, Nanjing 210094, China
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    Figures & Tables(6)
    The flowchart of noisy fringe image phase acquisition based on U-NET
    Different stages of high-frequency noisy fringe images restoration. (a) High-frequency noisy fringe image of the doll cat; (b) High-frequency noisy fringe image of the combined object; (c) The arc tangent function numerator of the doll cat; (d) The arc tangent function numerator of the combined object; (e) The arc tangent function denominator of the doll cat; (f) The arc tangent function denominator of the combined object; (g) The absolute phase of the doll cat; (h) The absolute phase of the combined object
    Comparison of the phase error between deep learning and traditional method. (a) The error between the true value and the absolute phase of the doll cat recovered by the traditional three-step phase shift method; (b) The error between the true value and the absolute phase of the doll cat using deep learning; (c) The error curve of the true value and the absolute phase of the doll cat recovered by the two methods; (d) The error between the true value and the absolute phase of the combined object recovered by the traditional three-step phase shift method; (e) The error between the true value and the absolute phase of combined objects using deep learning; (f) The error curve of the true value and the absolute phase of the combined object recovered by the two methods
    Comparison of 3D reconstruction results under different methods. (a) 3D reconstruction result of the doll cat restored by the method in this paper; (b) 3D reconstruction result of dolls cat restored by traditional methods; (c) The true 3D reconstruction result of the doll cat; (d) 3D reconstruction result of composite objects recovered by the method in this paper; (e) 3D reconstruction result of composite objects recovered by traditional methods; (f) The true 3D reconstruction result of the combined object
    3D reconstruction results comparison of fans at different speeds. (a) Fan images collected at first speed (about 800 rpm); (b) 3D reconstruction results of fan at first speed by the traditional three-step phase shift method; (c) 3D reconstruction results of fan at first speed based on U-Net network; (d) Fan images collected at second speed (about 1800 rpm); (e) 3D reconstruction results of fan at second speed with the traditional three-step phase shift method; (f) 3D reconstruction results of fan at second speed based on U-Net network
    3D reconstruction and analysis of precision sphere. (a) 3D reconstruction result of left precision sphere; (b) Error distribution of left precision sphere; (c) Error histogram of left precision sphere; (d) 3D reconstruction result of right precision sphere; (e) Error distribution of right precision sphere; (f) Error histogram of right precision sphere
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    Jiaye Wang, Yixuan Li, Yuzhen Zhang. A learning based on approach for noise reduction with raster images[J]. Infrared and Laser Engineering, 2022, 51(2): 20220006

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    Paper Information

    Category: Special issue-Computational optical imaging technology

    Received: Nov. 3, 2021

    Accepted: Feb. 14, 2022

    Published Online: Mar. 21, 2022

    The Author Email:

    DOI:10.3788/IRLA20220006

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