방송공학회논문지 (Journal of Broadcast Engineering)

  • 제18권3호
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  • Pages.463-473
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  • 2013
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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다시점 카메라를 이용한 초고해상도 영상 복원

Super-Resolution Image Reconstruction Using Multi-View Cameras

  • 안재균 (고려대학교 전기전자전파공학과) ;
  • 이준태 (고려대학교 전기전자전파공학과) ;
  • 김창수 (고려대학교 전기전자전파공학과)
  • Ahn, Jae-Kyun (School of Electrical Engineering, Korea University) ;
  • Lee, Jun-Tae (School of Electrical Engineering, Korea University) ;
  • Kim, Chang-Su (School of Electrical Engineering, Korea University)
  • 투고 : 2013.01.23
  • 심사 : 2013.05.09
  • 발행 : 2013.05.30
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초록

본 논문에서는 다시점 영상을 이용한 초고해상도 영상 복원 기법을 제안한다. 구체적으로 $5{\times}5$ 배열로 구성된 다시점 카메라로 25장의 영상을 취득하고, 가운데 카메라에 해당하는 초고해상도 영상을 저해상도 입력 영상과 24장의 저해상도 참조 영상을 활용하여 생성한다. 우선 입력 영상을 중심으로 스테레오 정합 기법을 이용하여 24개의 참조 영상에 대한 변이지도를 각각 추정한다. 그리고 저해상도 영상과 참조 영상에 있는 일치점들을 이용하여 초고해상도 영상을 복원한다. 최종적으로 반복적 균일화를 통해 초고해상도 영상을 보정한다. 실험을 통하여 본 논문에서 제안한 초고해상도 영상 복원 기법의 성능이 우수함을 확인한다.

In this paper, we propose a super-resolution (SR) image reconstruction algorithm using multi-view images. We acquire 25 images from multi-view cameras, which consist of a $5{\times}5$ array of cameras, and then reconstruct an SR image of the center image using a low resolution (LR) input image and the other 24 LR reference images. First, we estimate disparity maps from the input image to the 24 reference images, respectively. Then, we interpolate a SR image by employing the LR image and matching points in the reference images. Finally, we refine the SR image using an iterative regularization scheme. Experimental results demonstrate that the proposed algorithm provides higher quality SR images than conventional algorithms.

키워드

참고문헌

  1. D. Keren, S. Peleg, and R. Brada, "Image sequence enhancement using sub-pixel displacements," in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '88), pp. 742-746, June 1988.
  2. M. C. Hong, M. G. Kang, and A. K. Katsaggelos, "A regularized multichannel restoration approach for globally optimal high resolution video sequence," in Proc. of SPIE VCIP, vol. 3024, pp. 1306-1317, 1997. https://doi.org/10.1117/12.263211
  3. R. Y. Tsai and T. S. Huang, "Multiframe image restoration and registration," Advances in Computer Vision and Image Processing, JAI Press, Greenwich, Conn, USA, pp. 317-339, 1984.
  4. S. H. Rhee and M.G. Kang, "Discrete cosine transform based regularized high-resolution image reconstruction algorithm," Optical Engineering, vol. 38, no. 8, pp. 1348-1356, Aug. 1999. https://doi.org/10.1117/1.602177
  5. L. C. Pickup, D. P. Capel, S. J. Roberts, and A. Zisserman, "Bayesian methods for image super-resolution," The Computer Journal, Vol. 52, no. 1, pp. 101-113, 2009.
  6. S. Villena, M. Vega, R. Molina, and A. K. Katsaggelos, "Bayesian super- resolution image reconstruction using an l1 prior," in 6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009), pp. 152-157, 2009.
  7. S. D. Babacan, R. Molina, and A.K. Katsaggelos, "Variational bayesian super resolution," IEEE Trans. Image Process., Vol. 20, no. 4, pp. 984-999, 2011. https://doi.org/10.1109/TIP.2010.2080278
  8. T. Q. Pham, L. J. V. Vliet and K. Schutte, "Robust fusion of irregularly sampled data using adaptive normalized convolution," EURASIP Journal on Applied Signal Processing, Vol. 2006, pp. 1-12, 2006. https://doi.org/10.1155/ASP/2006/96421
  9. J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010. https://doi.org/10.1109/TIP.2010.2050625
  10. W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and super- resolution by adaptive sparse domain selection and adaptive regularization," IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838-1857, Jul. 2011. https://doi.org/10.1109/TIP.2011.2108306
  11. K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. https://doi.org/10.1109/TPAMI.2010.25
  12. Point Greay Research, "Triclops on-line manual," http://www.ptgrey. com/.
  13. Z. Y. Zhang, "Flexible camera calibration by viewing a plane from unknown orientations," in Proc. of International Conference on Computer Vision(ICCV), pp. 666-673, 1999.
  14. V. Kolmogorov and R. Zabih, "Computing visual correspondence with occlusions using graph cuts," in Proc. of International Conference on Computer Vision(ICCV), pp. 508-515, 2001.
  15. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE. Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004. https://doi.org/10.1109/TIP.2003.819861