Super Resolution based on Reconstruction Algorithm Using Wavelet basis

웨이브렛 기저를 이용한 초해상도 기반 복원 알고리즘

  • 백영현 (원광대학교 전자공학과) ;
  • 변오성 (원광대학교 전자공학과) ;
  • 문성룡 (원광대학교 전자공학과)
  • Published : 2007.01.25

Abstract

In most electronic imaging applications, image with high resolution(HR) are desired. HR means that pixel density within an image is high, and therefore HR image can offer more details that may be critical in various applications. Digital images that are captured by CCD and CMOS cameras usually have a very low resolution, which significantly limits the performance of image recognition systems. Image super-resolution techniques can be applied to overcome the limits of these imaging systems. Super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. To techniques were consisted of the registration algorithm for estimation and shift, the nearest neighbor interpolation using weight of acquired frames and presented frames. In this paper, it is proposed the image interpolation techniques using the wavelet base function. This is applied to embody a correct edge image and natural image when expend part of the still image by applying the wavelet base function coefficient to the conventional Super-Resolution interpolation method. And the proposal algorithm in this paper is confirmed to improve the image applying the nearest neighbor interpolation algorithm, bilinear interpolation algorithm.,bicubic interpolation algorithm through the computer simulation.

모든 전자 영상응용에는 고해상도 영상이 요구된다. 고해상도는 영상 안에 픽셀의 밀집도가 높음을 나타내며, 이로 인해 더 세밀하고 중요한 정보를 얻어 다양한 응용에 사용된다. 하지만 CCD 나 CMOS 카메라로 획득된 디지털 영상들은 영상인식 시스템 구현 시 많은 저해상도영상을 가지게 된다. 초해상도 기술은 이와 같은 한계를 넘어서서 영상인식시스템에 적용이 가능하다. 초해상도 기술은 다수의 영상으로부터 정보를 결합하여 해상도를 증가시키는 것으로써, 이 기술은 추정과 이동을 위한 정합알고리즘과 획득된 프레임과 현재 프레임의 가중치를 이용한 최소거리 이웃보간법으로 되어있다. 본 논문에서는 초해상도에 웨이브렛 변환 기저 함수 계수를 이용한 영상 보간 기법을 제안하고자 한다. 기존 초해상도 보간 방식 대신 웨이브렛 기저 계수를 적용한 B-스플라인 보간 함수를 이용하여, 움직이는 영상의 한 부분을 확대할 때 정확한 영상과 자연스러운 영상을 구현하기 위하여 적용하였다. 제안된 보간 알고리즘은 최소거리 이웃보간 알고리즘, bilinear 보간 알고리즘, bicubic 보간 알고리즘 적용한 확대 영상보다 우수한 결과를 얻었음을 모의실험을 통하여 확인하였다.

Keywords

References

  1. S. Park, M. Park, and M. Kang, 'Superresolution image reconstruction: A technical overview,' IEEE Signal Processing Magazine 20, pp. 2136, May 2003 https://doi.org/10.1109/MSP.2003.1203207
  2. M. Irani and S. Peleg, 'Improving resolution by image registration,' Computer Vision Graphical Image Processing: Graphical Models and Image Processing 53, pp. 231-239, 1991 https://doi.org/10.1016/1049-9652(91)90045-L
  3. R. R. Schultz and R. L. Stevenson, 'Extraction of high-resolution frames from video sequences,' IEEE Trans. Image Processing, vol. 5, pp. 9961011, June 1996 https://doi.org/10.1109/83.503915
  4. A. Rosenfeld, ed., Multiresolution Image Processing and Analysis, Springer-Verlag, Berlin/New York, 1984
  5. D. Capel and A. Zisserman, 'Super-resolution from multiple views using learnt image models,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Dec. 2001 https://doi.org/10.1109/CVPR.2001.991022
  6. K. Toraichi, S. Yang, M. Kamada, and R. Mori, 'Two-dimensional spline interpolation for image reconstruction,' Pattern Recog., Vol. 21, pp. 275-284, 1988 https://doi.org/10.1016/0031-3203(88)90062-3
  7. J. W. Go, K. H. Sohn, and C. H. Lee, 'Interpolation using Neural Networks for Digital Still Cameras,' IEEE Trans. Consumer Elec. Vol. 46, No. 3, pp. 610-616, Aug. 2000 https://doi.org/10.1109/30.883419
  8. Y. Y. Tang, L. H. Yang, and J. Liu, and H. Ma, Wavelet Theory and Its Application to Pattern Recognition, World Scientific, Vol. 36. 2000
  9. T. E. Boult, M.-C. Chiang, and R. J. Micheals, 'Super-resolution via image warping,' in Super-Resolution Imaging, E. S. Chaudhuri, Ed. Norwell, MA: Kluwer, 2001, pp. 131169
  10. M. Elad and A. Feuer, 'Restoration of a single superresolution image from several blurred, noisy and undersampled measured images,' IEEE Trans. Image Processing, vol. 6, pp. 16461658, Dec. 1997 https://doi.org/10.1109/83.650118
  11. B. C. Tom, A. K. Katsaggelos, and N. P. Galatsanos, 'Reconstruction of a high resolution image from registration and restoration of low resolution images,' in Proc. IEEE Int. Conf. Image Processing, Austin, TX, Nov. 1994, pp. 1316 https://doi.org/10.1109/ICIP.1994.413745
  12. D. Keren, S. Peleg, and R. Brada, 'Image sequence enhancement using sub-pixel displacements,' in IEEE Conference on Computer Vision and Pattern Recognition, pp. 742746, (Ann Arbor, MI), Jun. 1988 https://doi.org/10.1109/CVPR.1988.196317
  13. A. Rosenfeld, ed., Multiresolution Image Processing and Analysis, Springer-Verlag, Berlin/New York, 1984
  14. I. J. Schoenberg, Cardinal Spline Interpolation, Philadelphia, PA : SIAM, 1973
  15. M. Unser, A. Aldroubi, and M. Eden, 'Fast B-spline transforms for continuous image representation and interpolation,' IEEE Trans. https://doi.org/10.1109/34.75515