Advertisement

Inpainting in Multi-image Stereo

  • Arnav V. Bhavsar
  • Ambasamudram N. Rajagopalan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

In spite of numerous works on inpainting, there has been little work addressing both image and structure inpainting. In this work, we propose a new method for inpainting both image and depth of a scene using multiple stereo images. The observations contain unwanted artifacts, which can be possibly caused due to sensor/lens damage or occluders. In such a case, all the observations contain missing regions which are stationary with respect to the image coordinate system. We exploit the fact that the information missing in some images may be present in other images due to the motion cue. This includes the correspondence information for depth estimation/inpainting as well as the color information for image inpainting. We establish our approaches in the belief propagation (BP) framework which also uses the segmentation cue for estimation/inpainting of depth maps.

Keywords

Reference Image Depth Estimation Inpainted Image View Synthesis Reference View 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhou, C., Lin, S.: Removal of image artifacts due to sensor dust. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8 (2007)Google Scholar
  2. 2.
    Gu, J., Ramamoorthi, R., Belhumeur, P., Nayar, S.: Removing image artifacts due to dirty camera lenses and thin occluders. In: SIGGRAPH Asia 2909: ACM SIGGRAPH Asia 2009 papers, pp. 1–10 (2009)Google Scholar
  3. 3.
    Willson, R., Maimone, M., Johnson, A., Scherr, L.: An optical model for image artifacts produced by dust particles on lenses. In: International Symposium on Artificial Intelligence, Robtics and Automation in Space (2005)Google Scholar
  4. 4.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: SIGGRAPH 2000: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 417–424 (2000)Google Scholar
  5. 5.
    Criminisi, A., Perez, P., Toyama, K.: Object removal by exemplar-based inpainting. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), pp. 721–728 (2003)Google Scholar
  6. 6.
    Bhavsar, A.V., Rajagopalan, A.N.: Range map with missing data - joint resolution enhancement and inpainting. In: Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2008), pp. 359–365 (2008)Google Scholar
  7. 7.
    Mendez, T., Luz, A., Dudek, G.: Inter-image statistics for 3d environment modeling. Int. J. Comput. Vision 79(2) (2008)Google Scholar
  8. 8.
    Davis, J., Marschner, S., Garr, M., Levoy, M.: Filling holes in complex surfaces using volumetric diffusion. In: 3DPVT, pp. 428–438 (2002)Google Scholar
  9. 9.
    Cheng, C., Lin, S., Lai, S., Yang, J.: Improved novel view synthesis from depth image with large baseline. In: International Conference on Pattern Recognition, ICPR 2008 (2008)Google Scholar
  10. 10.
    Sahay, R., Rajagopalan, A.N.: Inpainting in shape from focus: Taking a cue from motion parallax. In: British Machine Vision Conference, BMVC 2009 (2009)Google Scholar
  11. 11.
    Wang, L., Jin, H., Yang, R., Gong, M.: Stereoscopic inpainting: Joint color and depth completion from stereo images. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)Google Scholar
  12. 12.
    Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 261–268 (2004)Google Scholar
  13. 13.
    Drouin, M., Trudeau, M., Roy, S.: Geo-consistency for wide multi-camera stereo. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 351–358 (2005)Google Scholar
  14. 14.
    Bhavsar, A.V., Rajagopalan, A.N.: Depth estimation with a practical camera. In: British Machine Vision Conference, BMVC 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Arnav V. Bhavsar
    • 1
  • Ambasamudram N. Rajagopalan
    • 1
  1. 1.Image Processing and Computer Vision Lab, Dept. of Electrical EngineeringIndian Institute of TechnologyMadras

Personalised recommendations