한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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국부 통계 특성을 이용한 적응 MAP 방식의 고해상도 영상 복원 방식

Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics

  • 발행 : 2006.12.30
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초록

본 논문에서는 국부 통계 특성을 이용한 적응 MAP 방식의 고해상도 영상 복원 알고리즘에 대해 제안한다. 고해상도 원 영상의 윤곽선을 보존하기 위해 저해상도 영상의 국부 특성을 이용하여 시각함수를 정의하였고, MAP(Maximum A Posteriori) 추정 방식을 이용하여 국부적인 열화 정도(smoothness)를 조절하였다. 또한 가중치가 부여된 함수를 이용하여 원 고해상도 영상에 가능한 가까운 최적의 해를 찾기 위하여 반복기법을 사용하였으며, 열화 요소는 매 반복 단계마다 부분적으로 복원된 고해상도 영상으로부터 이용하였다. 제안된 방식의 성능을 실험 결과를 통해 확인할 수 있었다.

In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve the edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution, and the smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.

키워드

참고문헌

  1. T. S. Huang Ed., Adavances in Computer Vision and Image Processing, JAI Press, 1984
  2. S. P. Kim, N. K. Bose, and H. M. Valenzuela, 'Recursive Reconstruction of High Resolutioin Image From Noisy Undersampled Multiframes,' IEEE Trans. Signal Processing, vol. 38, pp. 1013-1027, June 1990 https://doi.org/10.1109/29.56062
  3. A. K. Jain, Fundamentals of Digital Image Processing, New York: Prentice-Hall, 1989
  4. S. C. Park, M. K. Park, and M. G. Kang, 'Super-resolution image reconstruction: A Technical Overview,' IEEE Signal Processing Magazine, vol.20, no.3, pp.21-36, May 2003 https://doi.org/10.1109/MSP.2003.1203207
  5. A. N. Tikhonov and A. V. Gonchrsky, eds., Ill-Posed Problems in the Natural Science, MIP Pub., 1987
  6. M. Bertero, T. A. Poggio, and V. Torre, 'Ill-posed problems in early vision,' IEEE Proceeding, vol.76, no.8, pp.869-889, Aug. 1988 https://doi.org/10.1109/5.5962
  7. A. J. Patti, M. I. Sezan, and A. M. Tekalp, 'High-Resolution Image Reconstruction from A Low-Resolution Image sequence in the Presence of Time-Varying Motion Blur,' IEEE Proceeding of International Conference on Image Processing, pp. 343-347, Nov. 1994
  8. Y. Nakazawa, T. Saito, T. Komatsu, T. Skimori, and K. Aizawa, 'Two Approaches for Image Processing Based on High Resolution Image Acquisition,' IEEE Proceeding of International Conference on Image Processing, pp. 147-151, Nov. 1994
  9. N. K. Bose, H. C. Kim, and N. Bose, 'Constrained total least squares computations for high resolution image reconstruction with multisensors,' Int. J. Imaging Syst. Tech., vol. 12, pp. 35-42, 2002 https://doi.org/10.1002/ima.10004
  10. Michael Unser, Akram Aldroubi, and Murray Eden, 'B-Spline Signal Processing : Part I-Theory,' IEEE Trans. ASSP, vol. 41, no. 2. pp. 821-833, Feb. 1993 https://doi.org/10.1109/78.193220
  11. B. R. Schultz and F. K. Stevenson, 'A Bayesian Approach to Image Expansion for Improved Definition,' IEEE Trans. Image Processing, vol. 3, no. 3, pp. 996-1011, May 1994