Nothing Special   »   [go: up one dir, main page]
Skip to main content
EURASIP Journal on Advances in Signal Processing
Download PDF
  • Research Article
  • Open access
  • Published:

Single-Frame Image Super-resolution through Contourlet Learning

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 073767 (2006) Cite this article

Abstract

We propose a learning-based, single-image super-resolution reconstruction technique using the contourlet transform, which is capable of capturing the smoothness along contours making use of directional decompositions. The contourlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images, the inverse contourlet transform of which recovers the super-resolved image. In effect, we learn the high-resolution representation of an oriented edge primitive from the training data. Our experiments show that the proposed approach outperforms standard interpolation techniques as well as a standard (Cartesian) wavelet-based learning both visually and in terms of the PSNR values, especially for images with arbitrarily oriented edges.

References

  1. Do MN, Vetterli M: The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing 2005, 14(12):2091–2106.

    Article  Google Scholar 

  2. Tsai RY, Huang TS: Multiframe image restoration and registration. In Advances in Computer Vision and Image Processing. Volume 1. JAI Press, Greenwich, Conn, USA; 1984:317–339. chapter 7

    Google Scholar 

  3. Irani M, Peleg S: Improving resolution by image registration. CVGIP: Graphical Models and Image Processing 1991, 53(3):231–239. 10.1016/1049-9652(91)90045-L

    Google Scholar 

  4. Irani M, Peleg S: Motion analysis for image enhancement: resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 1993, 4(4):324–335. 10.1006/jvci.1993.1030

    Article  Google Scholar 

  5. Tekalp AM, Ozkan MK, Sezan MI: High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), March 1992, San Francisco, Calif, USA 3: 169–172.

    Google Scholar 

  6. Ng MK, Koo J, Bose NK: Constrained total least-squares computations for high-resolution image reconstruction with multisensors. International Journal of Imaging Systems and Technology 2002, 12(1):35–42. 10.1002/ima.10004

    Article  Google Scholar 

  7. Ng MK, Bose NK: Analysis of displacement errors in high-resolution image reconstruction with multisensors. IEEE Transactions on Circuits and SystemsPart I 2002, 49(6):806–813. 10.1109/TCSI.2002.1010035

    Article  Google Scholar 

  8. Ng MK, Bose NK: Fast color image restoration with multisensors. International Journal of Imaging Systems and Technoloy 2002, 12(5):189–197. 10.1002/ima.10028

    Article  Google Scholar 

  9. Nguyen N, Milanfar P, Golub G: A computationally efficient super-resolution image reconstruction algorithm. IEEE Transactions on Image Processing 2001, 10(4):573–583. 10.1109/83.913592

    Article  Google Scholar 

  10. Schultz RR, Stevenson RL: A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing 1994, 3(3):233–242. 10.1109/83.287017

    Article  Google Scholar 

  11. Rajan D, Chaudhuri S: An MRF-based approach to generation of super-resolution images from blurred observations. Journal of Mathematical Imaging and Vision 2002, 16(1):5–15. 10.1023/A:1013961817285

    Article  MathSciNet  Google Scholar 

  12. Rajan D, Chaudhuri S: Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(9):1102–1117. 10.1109/TPAMI.2003.1227986

    Article  Google Scholar 

  13. Elad M, Feuer A: Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images. IEEE Transactions on Image Processing 1997, 6(12):1646–1658. 10.1109/83.650118

    Article  Google Scholar 

  14. Lin Z, Shum H-Y: Fundamental limits of reconstruction-based super-resolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(1):83–97. 10.1109/TPAMI.2004.1261081

    Article  Google Scholar 

  15. Baker S, Kanade T: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(9):1167–1183. 10.1109/TPAMI.2002.1033210

    Article  Google Scholar 

  16. Capel D, Zisserman A: Super-resolution from multiple views using learnt image models. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 2: II-627–II-634.

    Google Scholar 

  17. Freeman WT, Jones TR, Pasztor EC: Example-based super-resolution. IEEE Computer Graphics and Applications 2002, 22(2):56–65. 10.1109/38.988747

    Article  Google Scholar 

  18. Hertzmann A, Jacobs CE, Oliver N, Curless B, Salesin DH: Image analogies. Proceedings of ACM SIGGRAPH '01, August 2001, Los Angeles, Calif, USA 327–340.

    Chapter  Google Scholar 

  19. Joshi MV, Chaudhuri S: A learning-based method for image super-resolution from zoomed observations. Proceedings of 5th International Conference on Advances in Pattern Recognition (ICAPR '03), December 2003, Calcutta, India 179–182.

    Google Scholar 

  20. Jiji CV, Joshi MV, Chaudhuri S: Single-frame image super-resolution using learned wavelet coefficients. International Journal of Imaging Systems and Technology 2004, 14(3):105–112. 10.1002/ima.20013

    Article  Google Scholar 

  21. Gunturk BK, Batur AU, Altunbasak Y, Hayes MH III, Mersereau RM: Eigenface-domain super-resolution for face recognition. IEEE Transactions on Image Processing 2003, 12(5):597–606. 10.1109/TIP.2003.811513

    Article  Google Scholar 

  22. Wang X, Tang X: Hallucinating face by eigentransformation with distortion reduction. Proceedings of 1st International Conference on Biometrics Authentication (ICBA '04), July 2004, Hong Kong 88–94.

    Chapter  Google Scholar 

  23. Jiji CV, Chaudhuri S: PCA-based generalized interpolation for image super-resolution. Proceedings of 4th Indian Conference on Vision, Graphics & Image Processing (ICVGIP '04), December 2004, Calcutta, India 139–144.

    Google Scholar 

  24. Pickup LC, Roberts SJ, Zisserman A: A sampled texture prior for image super-resolution. In Proceedings of Advances in Neural Information Processing Systems 16 (NIPS '03), 2004, Vancouver, British Columbia, Canada. Edited by: Thrun S, Saul L, Schölkopf B. MIT Press; 1587–1594.

    Google Scholar 

  25. Chang H, Yeung D-Y, Xiong Y: Super-resolution through neighbor embedding. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June–July 2004, Washington, DC, USA 1: I-275–I-282.

    Google Scholar 

  26. Begin I, Ferrie FR: Blind super-resolution using a learning-based approach. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 2: 85–89.

    Article  Google Scholar 

  27. Park J-S, Lee S-W: Enhancing low-resolution facial images using error back-projection for human identification at a distance. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 1: 346–349.

    Article  Google Scholar 

  28. Sun J, Zheng N-N, Tao H, Shum H-Y: Image hallucination with primal sketch priors. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03), June 2003, Madison, Wis, USA 2: II-729–II-736.

    Google Scholar 

  29. Wu J, Trivedi M, Rao B: Resolution enhancement by AdaBoost. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 893–896.

    Google Scholar 

  30. Do MN: Directional multiresolution image representations, M.S. thesis. Department of Communication, Swiss Federal Institute of Technology, Lausanne, Switzerland; November 2001.

    Google Scholar 

  31. Burt PJ, Adelson EH: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 1983, 31(4):532–540. 10.1109/TCOM.1983.1095851

    Article  Google Scholar 

  32. Bamberger RH, Smith MJT: A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing 1992, 40(4):882–893. 10.1109/78.127960

    Article  Google Scholar 

  33. Vetterli M: Multidimensional subband coding: some theory and algorithms. Signal Processing 1984, 6(2):97–112. 10.1016/0165-1684(84)90012-4

    Article  MathSciNet  Google Scholar 

  34. Park S-I, Smith MJT, Mersereau RM: A new directional filter bank for image analysis and classification. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 3: 1417–1420.

    Google Scholar 

  35. Shapiro JM: Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing 1993, 41(12):3445–3462. 10.1109/78.258085

    Article  Google Scholar 

  36. Vetterli M, Herley C: Wavelets and filter banks: theory and design. IEEE Transactions on Signal Processing 1992, 40(9):2207–2232. 10.1109/78.157221

    Article  Google Scholar 

  37. Phoong S-M, Kim CW, Vaidyanathan PP, Ansari R: A new class of two-channel biorthogonal filter banks and wavelet bases. IEEE Transactions on Signal Processing 1995, 43(3):649–665. 10.1109/78.370620

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India

    CV Jiji & Subhasis Chaudhuri

Authors
  1. CV Jiji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhasis Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Jiji, C., Chaudhuri, S. Single-Frame Image Super-resolution through Contourlet Learning. EURASIP J. Adv. Signal Process. 2006, 073767 (2006). https://doi.org/10.1155/ASP/2006/73767

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/ASP/2006/73767

Keywords