손실 정보 추정을 이용한 영상 보간과 휴대용 장치에서의 구현

Image Interpolation Using Loss Information Estimation and Its Implementation on Portable Device

  • Kim, Won-Hee (Department of Computer Engineering, PuKyong National University) ;
  • Kim, Jong-Nam (Department of Computer Engineering, PuKyong National University)
  • 발행 : 2010.03.25

초록

영상 보간법은 영상의 해상도를 향상시키기 위해서 사용되는 기술로서 보간 결과 영상에서 나타나는 화질 열화가 아직 까지 해결되지 않은 문제점이다. 이를 위해서 본 논문에서는 손실 정보 추정을 이용한 영상 보간법을 제안하고 제안한 알고리즘을 휴대용 장치에서 구현하였다. 제안하는 방법에서는 획득 저해상도 영상을 더욱 작은 크기로 축소한 후 다시 보간을 거쳐서 나온 영상을 이용해서 에러를 계산하고, 그 결과값을 보간하여 추정 손실 정보를 생성한다. 추정된 손실 정보는 적응적 가중치와 경합하여 최종적으로 보간된 고해상도 C영상에 더해지게 된다. 실험을 통해서 제안한 방볍이 기존의 알고리즘틀 보다 PSNR에서 2dB이상 향상된 것을 알 수 있었다. 또한 휴대용 장치에서 구현하여 실시간 처려가 가능한 것을 확인하였다. 이와 같이 제안한 방법은 영상의 확대와 영상 복원을 위한 다양한 응용 환경에서 사용될 수 있다.

An image interpolation is a technique to use for enhancement of image resolution, it have two problems which are image quality degradation of the interpolated result image and high computation complexity. In this paper, to solve the problem, we propose an image interpolation algorithm using loss information estimation and implement the proposed method on portable device. From reduction image of obtained low resolution image, the proposed method can computes error to use image interpolated and estimate loss information by interpolation of the computed error. The estimated loss information is added to interpolated high resolution image with weight factor. We verified that the proposed method has improved FSNR as 2dB than conventional algorithms by experiments. Also, we implemented the proposed method on portable device and checked up real-time action. The proposed algorithm may be helpful for various application for image enlargement and reconstruction.

키워드

참고문헌

  1. K. S. Ni and T. Q. Nguyen, "An Adaptable k-Nearest Neighbors Algorithm for MMSE Image Interpolation," IEEE Trans. Image Process., Vol. 18, Issue 9, pp. 1976-1987, Sep. 2009. https://doi.org/10.1109/TIP.2009.2023706
  2. L. Zhang and X. Wu, "Image Interpolation via Directional Filtering and Data Fusion," IEEE Transactions on Image Processing, Vol. 15, No. 8, pp. 2226-2238, Aug. 2006.
  3. J. W. Hwang and H. S. Lee, "Adaptive Image Interpolation Based on Local Gradient Features," IEEE Signal Processing Letters, Vol. 11, No. 3, pp. 359-362, Mar. 2004. https://doi.org/10.1109/LSP.2003.821718
  4. S. H. Hong, R. H. Park, S. J. Yang, and J. Y. Kim, "Image Interpolation Using Interpolative Classified Vector Quantization," Image Vis. Comput., Vol. 26, No. 2, pp. 228-239, Feb. 2008. https://doi.org/10.1016/j.imavis.2007.05.002
  5. O. Salvado, C. Hillenbrand, and D. Wilson. "Partial Volume Reduction by Interpolation with Reverse Diffusion," International Journal of Biomedical Imaging, Vol. 2006, pp. 1-13, 2006.
  6. Y. Bai and H. Zhuang, "On the Comparison of Bilinear, Cubic Spline, and Fuzzy Interpolation Techniques for Robotic Position Measurements," IEEE Transactions on Instrumentation and Measurement, Vol. 54, Issue 6, pp. 2281-2288, 2005. https://doi.org/10.1109/TIM.2005.858563
  7. H. Yoo, "Closed-form Least-squares Technique for Adaptive Linear Image Interpolation," Electronics Letters, Vol. 43, Issue 4, pp. 210-212, Feb. 2007. https://doi.org/10.1049/el:20073606
  8. W. Yu, "Colour Demosaicking Method Using Adaptive Cubic Convolution Interpolation with Sequential Averaging," IEE Proc.-Vis. Image Signal Process., Vol. 153, No. 5, Oct. 2006.
  9. S. G. Chang, Z. Cvetkovic, and M. Vetterli, "Locally Adaptive Wavelet-based Image Interpolation," IEEE Transactions on Image Processing, Vol. 15, Issue 6, pp. 1471-1485, Jun. 2006. https://doi.org/10.1109/TIP.2006.871162
  10. N. Asuni, "INEDI -- Tecnica Adattativa Per I'interpolazione di Immagini." Master's thesis, Universita degli Studi di Cagliari, 2007.
  11. A. Temizel and T. Vlachos, "Wavelet domain image resolution enhancement," IEE Proceedings Vision, Image and Signal Processing, Vol. 153, Issue 1, pp. 25-30, Feb. 2006. https://doi.org/10.1049/ip-vis:20045056
  12. A. Giachetti and N. Asuni, "Fast Artifacts-free Image Interpolation," In Proc. of the British Machine Vision Conf., pp. 123-132, 2008.
  13. S. C. Park, M. K. Park, and M. G. Kang, "Super-Resolution Image Reconstruction: A Technical Overview," IEEE Signal Processing Magazine , Vol. 20, Issue 3, pp. 21-36, May, 2003. https://doi.org/10.1109/MSP.2003.1203207