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Improved Image Restoration Algorithm about Vehicle Camera for Corresponding of Harsh Conditions

가혹한 조건에 대응하기 위한 차량용 카메라의 개선된 영상복원 알고리즘

  • Received : 2013.11.08
  • Published : 2014.02.25

Abstract

Vehicle Black Box (Event Data Recorder EDR) only recognizes the general surrounding environments of load. In addition, general EDR is difficult to recognize the images of a sudden illumination change. It appears that the lens is being a severe distortion. Therefore, general EDR does not provide the clues of the circumstances of the accident. To solve this problem, we estimate the value of Normalized Luminance Descriptor(NLD) and Normalized Contrast Descriptor(NCD). Illumination change is corrected using Normalized Image Quality(NIQ). Second, we are corrected lens distortion using model of Field Of View(FOV) based on designed method of fisheye lens. As a result, we propose integration algorithm of two methods that correct distortions of images using each Gamma Correction and Lens Correction in parallel.

자동차용 영상 사고기록장치(블랙박스)는 도로위의 일반적인 상황만을 촬영하게 된다. 또한, 급격한 조도변화의 상황에서는 주위의 환경을 제대로 인식하기 어렵고 렌즈 자체의 왜곡이 매우 심하기 때문에 사고 발생 시 명확한 증거로 사용하기 어렵다. 이러한 문제를 해결하기 위한 첫 번째 방법으로 정규화된 밝기 정보의 수표현자인 NLD(Normalized Luminance Descriptor)값과 정규화된 명암정보의 수표현자인 NCD(Normalized Contrast Descriptor)값을 정의하여 추출하고 두 값의 관계를 갖는 영상의 수표현자인 NIQ(Normalized Image Quality)값을 사용하여 급격한 조도변화에 대응하였다. 두 번째로, 어안렌즈가 디자인되는 방법을 기본으로 하는 FOV(Field Of View)모델을 이용하여 렌즈의 왜곡을 보정한다. 결과적으로 두 가지 영상왜곡은 각각 감마보정 및 렌즈왜곡보정의 영상처리 기법을 사용하여 병렬로 처리한 후 이를 하나의 영상으로 통합하는 알고리즘을 제안한다.

Keywords

References

  1. Traffic Accident Analysis System, http://taas.koroad.or.kr/bRead.sv?board_idt_cd=01&post_no=147&pageNum=1&category_cd=99
  2. J. P. Oakley and H. Bu, "Correction of simple contrast loss in color images," Image Processing, IEEE Transactions on, vol. 16, no. 2, pp. 511-522, Feb 2007. https://doi.org/10.1109/TIP.2006.887736
  3. N. S. Kopeika and J. Bordogna, "Background noise in optical communication systems," Proceedings of the IEEE, vol. 58, no. 10, pp. 1571-1577, Oct 1970. https://doi.org/10.1109/PROC.1970.7982
  4. J. P. Oakley and B. L. Satherley, "Improving image quality in poor visibility conditions using a physical model for contrast degradation," Image Processing, IEEE Transactions on, vol. 7, no. 2, pp. 167-179, Feb 1998. https://doi.org/10.1109/83.660994
  5. S. K. Nayar and S. G. Narasimhan, "Vision in bad weather," Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, vol. 2, pp. 820-827, Sep 1999.
  6. A. Restrepo (Palacios) and G. Ramponi, "Word Descriptors of Image Quality Based on Local Dispersion-versus-Location Distributions," 16th European Signal Processing Conference 2008, pp. 25-29, Aug 2008.
  7. S. B. Kang, "Semi-automatic methods for recovering radial distortion parameters from a single image," Technical Reports Series CRL 97/3, pp. 1-21, May 1997.
  8. B. K. Kim, "Radial Lens Distortion Correction in Digital Images," Proceeding of the 2010 Korea Signal Processing Conference, pp. 423-426, Oct 2010.
  9. S. M. Pizer et al, "Adaptive histogram equalization and its variations," Compututer Vision, Graphics, and Image Processing, vol. 39, pp. 355-368, Sep 1987. https://doi.org/10.1016/S0734-189X(87)80186-X
  10. K. Zuiderveld, "Contrast limited adaptive histogram equalization," Graphics Gems IV, pp. 474-485. 1994.
  11. J. A. Stark, "Adaptive image contrast enhancement using generalizations of histogram equalization," Image Processing, IEEE Transactions on, vol. 9, no. 5, pp. 889-896, May 2000. https://doi.org/10.1109/83.841534