Skip to main content


Superresolution under Photometric Diversity of Images


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.


  1. 1.

    MotionDSP 2007.

  2. 2.

    QELabs 2007.

  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

  4. 4.

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

  5. 5.

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

  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

  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.

  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.

  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.

  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.

  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.

  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

  13. 13.

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

  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

  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

  16. 16.

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

  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

  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.

  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.

  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.

  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.

  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

  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

  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

  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

  26. 26.

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

  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

  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.

  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

  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.

  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

  32. 32.

    Gevrekci M, Gunturk BK: Matlab user interface for super resolution image reconstruction for illumination varying and Bayer pattern images. 2007.

Download references

Author information

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 (, 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) doi:10.1155/2007/36076

Download citation


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