Limitations of Super Resolution Image Reconstruction and How to Overcome them for a Single Image

Seiichi Gohshi, Isao Echizen

Abstract

Super resolution image reconstruction (SRR) is a typical super resolution (SR) technology that has been researched with varying results. The SRR algorithm was initially proposed for still images. It uses many low-resolution images to reconstruct a high-resolution image. Unfortunately, in practice, we rarely have a sufficient number of low-resolution images for SRR to work. Usually, there is only one (or a few) blurry images. On the other hand, there is a need to improve blurry images in applications ranging from security and photo restoration to zooming functions and countless other examples related to the printing industry. Recently, SRR was extended to video sequences that have many similar frames that can be used as low-resolution images to reconstruct high-resolution frames. In normal SRR, one reconstructs a high-resolution image from lowresolution images sampled from one high-resolution image, but in the video application, the low-resolution video frames are not taken from higher resolution ones. This paper proposes a novel resolution improvement method that works without such a high- resolution image. Its algorithm is simple and can be applied to a single image and real-time video systems.

References

  1. Sung Cheol Park, Min Kyu Park and Moon Gi Kang, ”Super-Resolution Image Reconstruction: A Technical Overview”, IEEE Signal Processing Magazine, 1053-5888/03, pp. 21-36, May, 2003.
  2. S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, ”Fast and Robust Multi-frame Super-resolution”, IEEE Transactions on Image Processing , vol. 13, no. 10, pp. 1327- 1344, October, 2004.
  3. Adam W. M. van Eekeren, Klamer Schutte, and Lucas J. van Vliet, ”Multiframe Super-Resolution Reconstruction of Small Moving Objects”, IEEE Transactions on Image Processing, pp. 2901-2912, Vol. 19, No. 11, November, 2010.
  4. Aggelos Katsaggelos, Rafael Molina, and Javier Mateos, ”Super Resolution of Images and Video”, Synthesis Lectures on Images, Video and Multimedia Processing, Morgan& Clayppo Publishers, 2007.
  5. Xianghua Houa and Honghai Liu, ”Super-resolution Image Reconstruction for Video Sequence”, 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, pp. 4600-4603, 12-14 August, 2011.
  6. Matan Protter, Michael Elad, Hiroyuki Takeda, and Peyman Milanfar, ”Generalizing the Nonlocal-Means to Super-Resolution Reconstruction”, IEEE Transactions on Image Processing, pp. 36-51, Vol. 18, No. 1, Jan. 2009.
  7. D. Glasner, S. Bagon, and M. Irani, ”Super-Resolution from a Single Image”, International Conference on Computer Vision (ICCV), October 2009.
  8. S. Panda, R.S. Prasad, and G. Jena, ”POCS Based SuperResolution Image Reconstruction Using an Adaptive Regularization Parameter”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No. 2, September 2011, ISSN (Online), 1694-0814.
  9. N. Matsumoto and T. Ida, ”A Study on One Frame Reconstruction-based Super-resolution Using Image Segmentation”, IEICE Technical Report , SIP2008- 6,IE2008-6 (2008-04) (in Japanese).
  10. N. Matsumoto and T. Ida, ”Reconstruction Based SuperResolution Using Self-Congruency around Image Edges”, Journal of IEICE , Vol. J93-D, No. 2, pp. 118-126, Feb. 2010 (in Japanese).
  11. http://www.toshiba.co.jp/regza/lineup/xs5/quality 4k2k.html http://www.toshiba.co.jp/regza/detail/superresolution/reso lution.html(in Japanese)
  12. http://techon.nikkeibp.co.jp/english/NEWS EN/20110906/ 198008/
  13. Seiichi Gohshi and Isao Echizen, ”Subjective Assessment for HDTV with Super-Resolution function”, Seventh International Workshop on Video Processing and Quality Metrics for Consumer Electronics - (VPQM2013), Jan. 2013.
  14. Seiichi Gohshi, ”A New Signal Processing Method for Video” ACM Multimedia Systems 2012, Feb.2012. ACM Digital Library: MMSys 7812 Proceedings of the 3rd Multimedia Systems Conference, pp. 47-52 ACM New York, NY, USA c2012.
  15. W. F. Schreiber, ”Wirephoto Quality Improvement by Unsharp Masking”, J. Pattern Recognition, 2, 1970, pp. 111-121.
  16. J-S. Lee, ”Digital Image Enhancement and Noise Filtering by Use of Local Statistics”, IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-2, 2, March 1980, pp. 165-168.
  17. W. K. Pratt, ”Digital Image Processing (3rd Ed)”, John Wiley and Sons, 2001, pg. 278.
  18. Seiichi Gohshi, ”Limitation of Super Resolution Image Reconstruction for Video”, Computational Intelligence, Communication and Networks (CICSyN), Madrid, Jun. 2007.
  19. T. Kurita and Y. Sugiura, Consideration on Motion Compensated Deinterlacing and its Converter”, Technical Report of IEICE, CS93-148, IE93-85(1993-12). (in Japanese)
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Paper Citation


in Harvard Style

Gohshi S. and Echizen I. (2013). Limitations of Super Resolution Image Reconstruction and How to Overcome them for a Single Image . In Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013) ISBN 978-989-8565-74-7, pages 71-78. DOI: 10.5220/0004518300710078


in Bibtex Style

@conference{sigmap13,
author={Seiichi Gohshi and Isao Echizen},
title={Limitations of Super Resolution Image Reconstruction and How to Overcome them for a Single Image},
booktitle={Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)},
year={2013},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004518300710078},
isbn={978-989-8565-74-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)
TI - Limitations of Super Resolution Image Reconstruction and How to Overcome them for a Single Image
SN - 978-989-8565-74-7
AU - Gohshi S.
AU - Echizen I.
PY - 2013
SP - 71
EP - 78
DO - 10.5220/0004518300710078