Super-Resolution Algorithm Using Motion Estimation for Moving Vehicles

움직임 추정 기법을 이용한 움직이는 차량의 초고해상도 복원 알고리즘

  • Received : 2012.01.10
  • Accepted : 2012.05.16
  • Published : 2012.07.25

Abstract

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

본 논문은 움직임이 큰 저해상도 영상을 초고해상도 영상으로 복원하는 움직임 추정기반의 초고해상도 알고리즘을 제안한다. 일반적인 실험영상에 비해 실제 사용되는 움직임이 큰 영상은 부화소 움직임을 찾기가 어렵다. 또한 일반 움직임 추정기법을 이용한 참조이미지와 후보이미지를 찾기 위해서는 매우 높은 계산 복잡도를 가지는 단점이 있다. 이러한 문제점을 보완하기 위해 기존의 2차원적 움직임 추정기법을 이용하여 제안한 임계값을 기준으로 등록 조건을 만족하는 참조이미지를 결정하고, 후보 이미지들 사이의 최소 가중치를 가진 최적의 후보 이미지들을 찾아 초고해상도 복원과정을 진행하는 새로운 영상 등록 알고리즘을 제안하였다. 실험 결과에 따르면, 제안한 기법은 평균 PSNR이 31.89dB로 전통적인 초고해상도 기법보다 높은 PSNR을 보이며 계산 복잡도도 향상되는 결과가 나타났다.

Keywords

References

  1. R. Y. Tsai and T. S. Huang : Multiframe image restoration and registration. in Advances in Computer Vision and Image Processing. vol. 1, chapter 7, pp. 317-339, JAI Press, Greenwich, Conn, USA, 1984.
  2. P. Vandewalle, S. E. Susstrunk, and M. Vetterli.: Double resolution from a set of aliased images. in Proceedings of SPIE/IS&T Electronic Imaging 2004: Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V, vol. 5301 of Proceedings of SPIE, pp. 374-382, San Jose, Calif, USA, January 2004.
  3. D. Capel and A. Zisserman.: Computer vision applied to super-resolution. IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 75-86 2003. https://doi.org/10.1109/MSP.2003.1203211
  4. D. Keren, S. Peleg, and R. Brada.: Image sequence enhancement using sub-pixel displacements. in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 742-746, Ann Arbor, Mich, USA, June 1988.
  5. D. Rajan, S. Chaudhuri, and M. V. Joshi.: Multi-objective super-resolution: concepts and examples. IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 49-61 2003. https://doi.org/10.1109/MSP.2003.1203209
  6. M. V. Joshi, S. Chaudhuri, and R. Panuganti.: Superresolution imaging: use of zoom as a cue. Image and Vision Computing, vol. 22, no. 14, pp. 1185-1196 2004. https://doi.org/10.1016/j.imavis.2004.03.025
  7. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar.: Fast and robust multiframe super-resolution. IEEE Transactions on Image Processing, vol. 13, no. 10, pp. 1327-1344 2004. https://doi.org/10.1109/TIP.2004.834669
  8. M. Elad and A. Feuer.: Restoration of a single super-resolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing, vol. 6, no. 12, pp. 1646-1658 1997. https://doi.org/10.1109/83.650118
  9. S. Baker and T. Kanade.: Super-resolution optical flow. Technical Report CMU-RI-TR-99- 36, The Robotics Institute, Carnegie Mellon University 1999.
  10. E. S. Lee and M. G. Kang.: Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration. IEEE Trans. Image Processing, vol. 12, pp.826- 837 2003. https://doi.org/10.1109/TIP.2003.811488
  11. S. Baker and T. Kanade.: Limits on super resolution and how to break them. IEEE Trans.Pattern Analysis Machine Intelligence, vol. 24,pp. 1167-1183 2002. https://doi.org/10.1109/TPAMI.2002.1033210
  12. B. Marcel, M. Briot, and R. Murrieta, "Calcul de translationet rotation par la transformation de Fourier," Traitement duSignal, vol. 14, no. 2, pp. 135-149, 1997.
  13. H. S. Stone, M. T. Orchard, E.-C. Chang, and S. A. Martucci.: A fast direct Fourier-based algorithm for subpixel registration of images. IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2235-2243 2001. https://doi.org/10.1109/36.957286
  14. S.P. Kim, N.K. Bose, and H.M. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Trans. Acoust., Speech, Signal Proc., vol. 38, pp. 1013-1027, June 1990. https://doi.org/10.1109/29.56062
  15. W. Freeman, T. Jones, and E. Pasztor, "Example-based super-resolution," IEEE Computer Graphics and Applications, vol. 40, no.1, pp. 23-47, 2000.
  16. Hanisch, R.J., R.L. White, and R.L. Gilliland. "Deconvolution of Hubble Space Telescope Images and Spectra." Deconvolution of Images and Spectra (P.A. Jansson, ed.). Boston, MA: Academic Press, 1997, pp. 310-356.
  17. 백영현, 변오성, 문성룡, "웨이브렛 기저를 이용한 초해상도 기반 복원 알고리즘," 대한전자공학회, 전자공학회논문지-SP, 제44권 제1호 (통권 제313 호), 17-25쪽, 2007년 1월