Proposed Framework

  • Ricard PradosEmail author
  • Rafael Garcia
  • László Neumann
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


This chapter describes the full photo-mosaicing pipeline proposed in this monograph. This pipeline is intended to process datasets of thousands of images from large-scale underwater optical surveys. The first stages of the process involve the input sequence preprocessing, required to reduce artifacts such as the inhomogeneous lighting of the images, mainly due to the use of limited-power artificial light sources and the phenomenon of light attenuation and scattering. In this step, a context-dependent gradient based image enhancement is proposed, with allows equalizing the appearance of neighboring images when those have been acquired at different depths of with different exposure times. The pipeline follows with the selection of each image contribution to the final mosaic, based on different criteria, such as image quality and acquisition distance. Next, the optimal seam placement for all the images is found. A gradient blending, in a narrow region around the optimally found seam, is applied in order minimize the visibility of the joining regions, as well as to refine the appearance equalization along all the involved images. Finally, a novel strategy allowing to process giga-mosaics composed of tenths of thousands of images in conventional hardware is proposed. The technique divides the whole mosaic in tiles, processing them individually and seamlessly blending all of them again using a technique that requires low computational resources.


Image preprocessing Inhomogeneous lighting compensation Image enhancement Gradient domain blending Tone mapping Giga-mosaicing 


  1. 1.
    Goldstein, E.B.: Sensation and perception. In: PSY 385 Perception Series. Cengage Learning, Stamford (2010)Google Scholar
  2. 2.
    Garcia, R., Nicosevici, T., Cufi, X.: On the way to solve lighting problems in underwater imaging. In: Proceedings of the MTS/IEEE OCEANS Conference, vol. 2, pp. 1018–1024, Oct 2002Google Scholar
  3. 3.
    Capel, D.: Image Mosaicing and Super-Resolution. Springer, Berlin (2004)Google Scholar
  4. 4.
    Rzhanov, Y., Gu, F.: Enhancement of underwater videomosaics for post-processing. In: Proceedings of the MTS/IEEE OCEANS Conference, pp. 1–6, Oct 2007Google Scholar
  5. 5.
    Gracias, N., Negahdaripour, S., Neumann, L., Prados, R., Garcia, R.: A motion compensated filtering approach to remove sunlight flicker in shallow water images. In: Proceedings of the IEEE OCEANS Conference, pp. 1–7, Sept 2008Google Scholar
  6. 6.
    Chan, T., Jianhong, S.: Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. Society for Industrial and Applied Mathematics, Philadelphia (2005)Google Scholar
  7. 7.
    Guillemaud, R.: Uniformity correction with homomorphic filtering on region of interest. In: Proceedings of the International Conference on Image Processing (ICIP), pp. 872–875 (1998)Google Scholar
  8. 8.
    Ebner, M.: Color constancy. In: The Wiley-IS&T Series in Imaging Science and Technology. Wiley, New Jeresy (2007)Google Scholar
  9. 9.
    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)Google Scholar
  10. 10.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41 (2001)Google Scholar
  11. 11.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, Manchester, UK, pp. 189–192, Aug 1988Google Scholar
  12. 12.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)Google Scholar
  13. 13.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: European Conference on Computer Vision, pp. 404–417 (2006)Google Scholar
  14. 14.
    Ferrer, J., Elibol, A., Delaunoy, O., Gracias, N., Garcia, R.: Large-area photo-mosaics using global alignment and navigation data. In: Proceedings of the IEEE OCEANS Conference, pp. 1–9, Oct 2007Google Scholar
  15. 15.
    Elibol, A., Garcia, R., Delaunoy, O., Gracias, N.: A new global alignment method for feature based image mosaicing. In: Proceedings of the International Symposium on Advances in Visual Computing (ISVC), Part II, pp. 257–266. Springer, Berlin, Heidelberg (2008)Google Scholar
  16. 16.
    Gracias, N., Mahoor, M., Negahdaripour, S., Gleason, A.: Fast image blending using watersheds and graph cuts. Image Vis. Comput. 27, 597–607 (2009)Google Scholar
  17. 17.
    Kazhdan, M., Hoppe, H.: Streaming multigrid for gradient-domain operations on large images. ACM Trans. Graph. (SIGGRAPH) 27(3), 1–10 (2008)CrossRefGoogle Scholar
  18. 18.
    Neumann, L., Matkovic, K., Purgathofer, W.: Automatic exposure in computer graphics based on the minimum information loss principle. In: IEEE Computer Society, Hannover, Germany, vol. 0, pp. 666–667 (1998)Google Scholar
  19. 19.
    Vitter, J.S.: External memory algorithms and data structures: dealing with massive data. ACM Comput. Surv. (CSUR) 33(2), 209–271 (2001)Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Ricard Prados
    • 1
    Email author
  • Rafael Garcia
    • 1
  • László Neumann
    • 1
  1. 1.University of GironaGironaSpain

Personalised recommendations