Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

Zoom Lens Distortion Correction Of Video Sequence Using Nonlinear Zoom Lens Distortion Model

비선형 줌-렌즈 왜곡 모델을 이용한 비디오 영상에서의 줌-렌즈 왜곡 보정

  • Kim, Dae-Hyun (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Shin, Hyoung-Chul (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Oh, Ju-Hyun (KBS Broadcast Technical Research Institute) ;
  • Nam, Seung-Jin (KBS Broadcast Technical Research Institute) ;
  • Sohn, Kwang-Hoon (School of Electrical and Electronic Engineering, Yonsei University)
  • 김대현 (연세대학교 전기전자공학부) ;
  • 신형철 (연세대학교 전기전자공학부) ;
  • 오주현 (KBS 방송기술연구소) ;
  • 남승진 (KBS 방송기술연구소) ;
  • 손광훈 (연세대학교 전기전자공학부)
  • Published : 2009.05.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we proposed a new method to correct the zoom lens distortion for the video sequence captured by the zoom lens. First, we defined the nonlinear zoom lens distortion model which is represented by the focal length and the lens distortion using the characteristic that lens distortion parameters are nonlinearly and monotonically changed while the focal length is increased. Then, we chose some sample images from the video sequence and estimated a focal length and a lens distortion parameter for each sample image. Using these estimated parameters, we were able to optimize the zoom lens distortion model. Once the zoom lens distortion model was obtained, lens distortion parameters of other images were able to be computed as their focal lengths were input. The proposed method has been made experiments with many real images and videos. As a result, accurate distortion parameters were estimated from the zoom lens distortion model and distorted images were well corrected without any visual artifacts.

본 논문은 줌-렌즈로 취득한 비디오 영상에 대해서 줌-렌즈의 왜곡을 자동으로 보상할 수 있는 새로운 방법을 제안하였다. 먼저, 초점거리의 증가에 따라 렌즈의 왜곡 계수가 비선형적으로 단조 감소하는 특징으로부터 초점거리와 렌즈 왜곡 계수로 표현되는 비선형 줌-렌즈 왜곡 모델을 정의하였다. 그리고 취득한 비디오 영상으로부터 몇 장의 샘플 영상을 선정하고, 이 샘플영상에 대한 초점거리와 렌즈 왜곡 계수는 기존의 방법들을 이용하여 측정하였다. 이렇게 측정한 초점거리와 렌즈 왜곡 계수들로 부터 줌-렌즈 왜곡 모델을 최적화 시켰다. 최적화된 줌-렌즈 왜곡 모델은 각 비디오 영상의 초점거리를 입력으로 하여 렌즈 왜곡계수를 자동으로 계산할 수 있다. 본 논문에서 제안한 방법은 다양한 실사 영상과 비디오 영상에 적용하여 그 성능을 검증하였으며, 화질의 열화 없이 영상의 왜곡을 보상할 수 있었다.

Keywords

References

  1. S. W. Shih, Y. P. Hung, and W.S. Lin. When should we consider lens distortion in camera calibration. Pattern Recognition, 28(3):447-461, 1995 https://doi.org/10.1016/0031-3203(94)00107-W
  2. R. Tsai. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robot. Autom., 3(4):1165–1172, Aug. 1987
  3. J. Weng, P. Cohen, and M. Herniou. Camera calibration with distortion models and accuracy evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(10):965–980, Oct. 1992 https://doi.org/10.1109/34.159901
  4. D. Brown. Close-range camera calibration. Photogrammetry Engineering, 37(8):855–866, Aug. 1971
  5. S. Kang. Semi-automatic methods for recovering radial distortion parameters from a single image. Cambridge Research Laboratory, Technical ReportCRL97/3, May1997
  6. F. Devernay and O. Faugeras. Straight lines have to be straight: automatic calibration and removal of distortion from scenes of structured environments. Machine Vision Application, 1:14–24,2001 https://doi.org/10.1007/PL00013269
  7. S. Becker and V. Bove. Semi-automatic 3D model extraction from uncalibrated 2D camera views. Visual Data Exploration and Analysis II (SPIE), 2:447–461, Feb. 1995 https://doi.org/10.1117/12.205979
  8. M. Penna. Camera calibration: a quick and easy way to determine the scale factor. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(10):1240–1245, Oct. 1991 https://doi.org/10.1109/34.107007
  9. Z. Zhang. On the epipolar geometry between two images with lens distortion. Int. Conf. on Pattern Recognition, 1:407–411, Aug. 1996 https://doi.org/10.1109/ICPR.1996.546059
  10. G. Stein. Lens distortion calibration using point correspondences. IEEE Int. Conf. on Computer Vision and Pattern Recognition, 1:143 –148, Jun. 1997
  11. A.W. Fitzgibbon, Simultaneous linear estimation of multiple view geometry and lens distortion. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), 2001 https://doi.org/10.1109/CVPR.2001.990465
  12. M. Li and J.M. Lavest. Some aspects of zoom lens camera calibration. IEEE Trans. on Pattern Recognition and Machine Intelligence, 18(11), Nov. 1996 https://doi.org/10.1109/34.544080
  13. H. A. Beyer. Accurate calibration of CCD-cameras. Int. Conf. on Computer Vision and Pattern Recognition, June 1992
  14. O. Faugeras and G. Toscani. Structure from Motion using the Reconstruction and Reprojection Technique. IEEE Workshop on Computer Vision, 345-348, 1987
  15. http://en.wikipedia.org/wiki/Zoom_lens
  16. R. Kingslake. The development of the zoom lens. Journal of the SMPTE 69, 534
  17. D. Clark.ZoomLenses, Monographson AppliedOptics, 7, Adam-Hildger(London)
  18. http://www.adaptall-2.com/articles/InsideZoomLens/InsideZoomLens.html
  19. S. J. Nam. Development of POVIS (POst Virtual Imaging System). KBS Technical Report, pp.248-256, 2007
  20. http://www.photoshopcafe.com/tutorials/lens_distortion/lens.htm
  21. B. Triggs. The Absolute Quadric. IEEE International Conference on Computer Vision and Pattern Recognition, pp. 609-614, 1997 https://doi.org/10.1109/CVPR.1997.609388
  22. http://epaperpress.com/ptlens/