Skip to main content

Superresolution under Photometric Diversity of Images

Abstract

Superresolution (SR) is a well-known technique to increase the quality of an image using multiple overlapping pictures of a scene. SR requires accurate registration of the images, both geometrically and photometrically. Most of the SR articles in the literature have considered geometric registration only, assuming that images are captured under the same photometric conditions. This is not necessarily true as external illumination conditions and/or camera parameters (such as exposure time, aperture size, and white balancing) may vary for different input images. Therefore, photometric modeling is a necessary task for superresolution. In this paper, we investigate superresolution image reconstruction when there is photometric variation among input images.

References

  1. 1.

    MotionDSP 2007.https://doi.org/www.motiondsp.com/

  2. 2.

    QELabs 2007.https://doi.org/www.qelabs.com/

  3. 3.

    Park SC, Park MK, Kang MG: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 2003,20(3):21-36. 10.1109/MSP.2003.1203207

    Article  Google Scholar 

  4. 4.

    Chaudhuri S (Ed): Super-Resolution Imaging. Springer, Berlin, Germany; 2001.

    Google Scholar 

  5. 5.

    Capel D: Image Mosaicing and Super-Resolution. Springer, Berlin, Germany; 2004.

    Google Scholar 

  6. 6.

    Farsiu S, Robinson D, Elad M, Milanfar P: Advances and challanges in super-resolution. International Journal of Imaging Systems and Technology 2004,14(2):47-57. 10.1002/ima.20007

    Article  Google Scholar 

  7. 7.

    Borman S, Stevenson RL: Super-resolution from image sequences: a review. Proceedings of the Midwest Symposium on Circuits and Systems, April 1998, Notre Dame, Ind, USA 5: 374–378.

    Google Scholar 

  8. 8.

    Reinhard E, Ward G, Pattanaik S, Debevec P: High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann, San Francisco, Calif, USA; 2006.

    Google Scholar 

  9. 9.

    Debevec PE, Malik J: Recovering high dynamic range radiance maps from photographs. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '97), August 1997, Los Angeles, Calif, USA 369–378.

    Google Scholar 

  10. 10.

    Mann S, Picard RW: Being 'undigital' with digital cameras: extending dynamic range by combining differently exposed pictures. Proceedings of the 48th IS&T's Annual Conference, May 1995, Washington, DC, USA 442–448.

    Google Scholar 

  11. 11.

    Robertson MA, Borman S, Stevenson RL: Dynamic range improvement through multiple exposures. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 159–163.

    Article  Google Scholar 

  12. 12.

    Capel D, Zisserman A: Computer vision applied to super resolution. IEEE Signal Processing Magazine 2003,20(3):75-86. 10.1109/MSP.2003.1203211

    Article  Google Scholar 

  13. 13.

    Gunturk BK, Gevrekci M: High-resolution image reconstruction from multiple differently exposed images. Signal Processing Letters 2006,13(4):197-200.

    Article  Google Scholar 

  14. 14.

    Litvinov A, Schechner YY: Radiometric framework for image mosaicking. Journal of the Optical Society of America A 2005,22(5):839-848. 10.1364/JOSAA.22.000839

    Article  Google Scholar 

  15. 15.

    Hasler D, Süsstrunk S: Mapping colour in image stitching applications. Journal of Visual Communication and Image Representation 2004,15(1):65-90. 10.1016/j.jvcir.2003.06.001

    Article  Google Scholar 

  16. 16.

    Nayar SK, Ikeuchi K, Kanade T: Shape from interreflections. International Journal of Computer Vision 1991,6(3):173-195. 10.1007/BF00115695

    Article  Google Scholar 

  17. 17.

    Grossberg MD, Nayar SK: Determining the camera response from images: what is knowable? IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(11):1455-1467. 10.1109/TPAMI.2003.1240119

    Article  Google Scholar 

  18. 18.

    Mann S, Manders C, Fung J: Painting with looks: photographic images from video using quantimetric processing. Proceedings of the 10th ACM International Conference on Multimedia (MULTIMEDIA '02), December 2002, Juan les Pins, France 117–126.

    Google Scholar 

  19. 19.

    Mann S, Mann R: Quantigraphic imaging: estimating the camera response and exposures from differently exposed images. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 842–849.

    Google Scholar 

  20. 20.

    Mitsunaga T, Nayar SK: Radiometric self calibration. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 1: 374–380.

    Article  Google Scholar 

  21. 21.

    Tsin Y, Ramesh V, Kanade T: Statistical calibration of CCD imaging process. Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 1: 480–487.

    Google Scholar 

  22. 22.

    Mann S: Comparametric equations with practical applications in quantigraphic image processing. IEEE Transactions on Image Processing 2000,9(8):1389-1406. 10.1109/83.855434

    MathSciNet  Article  Google Scholar 

  23. 23.

    Patti AJ, Sezan MI, Tekalp AM: Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Transactions on Image Processing 1997,6(8):1064-1076. 10.1109/83.605404

    Article  Google Scholar 

  24. 24.

    Schultz RR, Stevenson RL: Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 1996,5(6):996-1011. 10.1109/83.503915

    Article  Google Scholar 

  25. 25.

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

    Google Scholar 

  26. 26.

    Fattal R, Lischinski D, Werman M: Gradient domain high dynamic range compression. ACM Transactions on Graphics 2002,21(3):249-256.

    Article  Google Scholar 

  27. 27.

    Kang SB, Uyttendaele M, Winder S, Szeliski R: High dynamic range video. ACM Transactions on Graphics 2003,22(3):319-325. 10.1145/882262.882270

    Article  Google Scholar 

  28. 28.

    Ward G: Fast, robust image registration for compositing high dynamic range photographs from hand-held exposures. Journal of Graphics Tools 2003,8(2):17-30.

    Article  Google Scholar 

  29. 29.

    Candocia FM: Jointly registering images in domain and range by piecewise linear comparametric analysis. IEEE Transactions on Image Processing 2003,12(4):409-419. 10.1109/TIP.2003.811497

    Article  Google Scholar 

  30. 30.

    Harris CG, Stephens M: A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference, August-September 1988, Manchester, UK 147–151.

    Google Scholar 

  31. 31.

    Fischler MA, Bolles RC: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 1981,24(6):381-395. 10.1145/358669.358692

    MathSciNet  Article  Google Scholar 

  32. 32.

    Gevrekci M, Gunturk BK: Matlab user interface for super resolution image reconstruction for illumination varying and Bayer pattern images. 2007.https://doi.org/www.ece.lsu.edu/ipl/Demos.html

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Murat Gevrekci.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/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

Gevrekci, M., Gunturk, B.K. Superresolution under Photometric Diversity of Images. EURASIP J. Adv. Signal Process. 2007, 036076 (2007). https://doi.org/10.1155/2007/36076

Download citation

Keywords

  • Information Technology
  • Exposure Time
  • Image Reconstruction
  • Quantum Information
  • Input Image