张全法, 杜丽丽, 申杰. 书籍扫描图像畸变参数自动计算方法的研究[J]. 应用光学, 2006, 27(6): 516-519.
引用本文: 张全法, 杜丽丽, 申杰. 书籍扫描图像畸变参数自动计算方法的研究[J]. 应用光学, 2006, 27(6): 516-519.
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.

书籍扫描图像畸变参数自动计算方法的研究

Auto determination of distortion parameters for correction of scanned book image

  • 摘要: 为实现书籍扫描图像的畸变自动校正,提出用多项式来描述各像素的理论灰度g(zi)与页面上对应点到扫描仪工作平面距离zi二者之间的关系。为确立该多项式,在畸变参数已知条件下扫描一幅图像,根据已知畸变参数求出zi,即可按最小二乘法原理由各像素灰度的实际值求出多项式的各个系数。实验证明,采用4阶多项式已能满足一般要求,并求出了各系数。对任意扫描图像,自动计算畸变参数的方法为:首先利用扫描图像上页边空白处各像素的灰度,对畸变参数进行估计,并求出zi的估计值;然后代入所确立的多项式,可求得g(zi);通过调整各畸变参数的估计值,直到g(zi)与gi最为接近,即得最佳畸变参数。用于图像校正实验,获得了较好的校正效果,最大误差由不校正时的41%下降到了6.9%。这使得无需用户测量并输入有关畸变参数即可进行自动校正。

     

    Abstract: 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.

     

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