ZHANG Quan-fa, DU Li-li, SHEN Jie. Auto determination of distortion parameters for correction of scanned book image[J]. Journal of Applied Optics, 2006, 27(6): 516-519.
Citation: ZHANG Quan-fa, DU Li-li, SHEN Jie. Auto determination of distortion parameters for correction of scanned book image[J]. Journal of Applied Optics, 2006, 27(6): 516-519.

Auto determination of distortion parameters for correction of scanned book image

More Information
  • Corresponding author:

    ZHANG Quan-fa

  • Received Date: July 22, 2005
  • Revised Date: November 13, 2005
  • To correct the distortion of scanned book image, a polynomial is proposed to describe the relation between g(zi) and zi, where g(zi) is the grayscale of a pixel and ziis the distance from the pixel′s corresponding point to the scanner′s operation plane. With an image of a twisted plane object whose distortion parameters are known, zican be calculated and the polynomial can be established by the leastsquare method using the actual grayscale gi of every pixel. The experiment indicates, accuracy of the polynomial is good enough when q=4, and every coefficient is computed for later use. For distorted image of plane object twisted at will, distortion parameters are calculated automatically as follows, distortion parameters are estimated by using grayscale of pixels located at the book margin, and the estimation of zi is calculated, and zi is used in the established polynomial to calculate g(zi); the optimal distortion parameters are found by adjusting the parameters until every calculated g(zi) is close to gi. In the experiment, good image correction results are achieved with the parameters obtained by this method. The maximum error is reduced from 41 percent to 6.9 percent. Thus, image correction can be done automatically without manual operation.
  • Related Articles

    [1]JIANG Han, WU Jun. Infrared image enhancement algorithm based on secondary guided filtering and its implementation on FPGA[J]. Journal of Applied Optics, 2023, 44(4): 777-785. DOI: 10.5768/JAO202344.0402002
    [2]ZHANG Zeyu, ZHANG Hong, WU Lingfan, YANG Yifan, LI Xuliang. FPGA-based real-time Bayer demosaicking algorithm and its implementation[J]. Journal of Applied Optics, 2022, 43(2): 240-247. DOI: 10.5768/JAO202243.0202002
    [3]LI Yawei, ZHANG Hong, WU Lingfan, YANG Yifan, CHEN Hao. FPGA-based real-time atmospheric turbulence image restoration algorithm and its implementation[J]. Journal of Applied Optics, 2021, 42(6): 1017-1024. DOI: 10.5768/JAO202142.0602002
    [4]MAO Xinrong, LIU Kaiming, WANG Leyi, HAN Xiaobing. Image distortion correction algorithm based on FPGA[J]. Journal of Applied Optics, 2020, 41(1): 86-93. DOI: 10.5768/JAO202041.0102004
    [5]YE Mao, HUANG Pingao, LYU Yang, TANG Ning, LIU Jianwei. Research and implementation of real-time fog and haze video image restoration system based on FPGA[J]. Journal of Applied Optics, 2019, 40(5): 812-817. DOI: 10.5768/JAO201940.0502004
    [6]ZHU Zhen, XU Kailuan, LIU Bing. FPGA and DSP based Micro-INS[J]. Journal of Applied Optics, 2011, 32(4): 602-606.
    [7]HOU Feng-qian, NING Zi-li, BI Bo-rui. Implementation of plesiochronous digital multiplexer based on FPGA for the FSO system[J]. Journal of Applied Optics, 2010, 31(2): 180-184.
    [8]HE Ming, WANG Xin-sai, LI Jian, LI Zhi-jun. FPGA implementation of infrared image preprocessing with cyclic lateral inhibition network[J]. Journal of Applied Optics, 2008, 29(3): 368-373.
    [9]LIU Jian-peng, CHEN Wei-dong, QIAN Jun. Design of real-time median filter based on FPGA[J]. Journal of Applied Optics, 2007, 28(6): 712-715.
    [10]PENG Fu-lun. Four-channel Linear Array CCD-IT-P1 Driven by CPLD[J]. Journal of Applied Optics, 2005, 26(1): 56-59.

Catalog

    Article views (3385) PDF downloads (1065) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return