Accurate Disparity Estimation for Plenoptic Images

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• 作者：Neus Sabater (16)
  Mozhdeh Seifi (16)
  Valter Drazic (16)
  Gustavo Sandri (16)
  Patrick P茅rez (16)
  
  16. Technicolor ; 975 Av. des Champs Blancs ; 35576 ; Cesson-S茅vign茅 ; France
  
• 关键词：Plenoptic camera ; Raw ; data conversion ; Disparity estimation
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8926
• 期：1
• 页码：548-560
• 全文大小：3,929 KB
• 参考文献：1. http://code.behnam.es/python-lfp-reader/
  2. http://optics.miloush.net/lytro/
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  4. Bishop, TE, Favaro, P Full-Resolution Depth Map Estimation from an Aliased Plenoptic Light Field. In: Kimmel, R, Klette, R, Sugimoto, A eds. (2011) Computer Vision 鈥?ACCV 2010. Springer, Heidelberg, pp. 186-200 CrossRef
  5. Bishop, TE, Favaro, P (2012) The light field camera: Extended depth of field, aliasing, and superresolution. TPAMI 34: pp. 972-986 CrossRef
  6. Cho, D., Lee, M., Kim, S., Tai, Y.W.: Modeling the calibration pipeline of the lytro camera for high quality light-field image reconstruction. In: ICCV (2013)
  7. Dansereau, D.G., Pizarro, O., Williams, S.B.: Decoding, calibration and rectification for lenselet-based plenoptic cameras. In: CVPR (2013)
  8. Drazic, V., Sabater, N.: A precise real-time stereo algorithm. In: ACM Conf. on Image and Vision Computing New Zealand, pp. 138鈥?43 (2012)
  9. Fiss, J., Curless, B., Szeliski, R.: Refocusing plenoptic images using depth-adaptive splatting. In: ICCP (2014)
  10. Georgiev, T., Chunev, G., Lumsdaine, A.: Superresolution with the focused plenoptic camera. In: SPIE Electronic Imaging (2011)
  11. Georgiev, T., Yu, Z., Lumsdaine, A., Goma, S.: Lytro camera technology: theory, algorithms, performance analysis. In: SPIE Electronic Imaging, pp. 86671J鈥?6671J (2013)
  12. Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: Conf. on Computer Graphics and Interactive Techniques (1996)
  13. Heber, S, Ranftl, R, Pock, T Variational Shape from Light Field. In: Heyden, A, Kahl, F, Olsson, C, Oskarsson, M, Tai, X-C eds. (2013) Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer, Heidelberg, pp. 66-79 CrossRef
  14. Kim, C., Zimmer, H., Pritch, Y., Sorkine-Hornung, A., Gross, M.: Scene reconstruction from high spatio-angular resolution light fields. ACM Trans. Graph. 32(4), 73 (2013)
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  16. Lumsdaine, A., Georgiev, T.: The focused plenoptic camera. In: ICCP (2009)
  17. Ng, R.: Digital light field photography. Ph.D. thesis, Stanford University (2006)
  18. Perez, F., Perez, A., Rodriguez, M., Magdaleno, E.: Fourier slice super-resolution in plenoptic cameras. In: ICCP (2012)
  19. Sabater, N, Morel, JM, Almansa, A (2011) How accurate can block matches be in stereo vision?. SIAM Journal on Imaging Sciences 4: pp. 472-500 CrossRef
  20. Scharstein, D, Szeliski, R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47: pp. 7-42 CrossRef
  21. Seifi, M., Sabater, N., Drazic, V., Perez, P.: Disparity-guided demosaicing of light-field images. In: ICIP (2014)
  22. Tao, M., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: ICCV (2013)
  23. Tulyakov, S., Lee, T., H., H.: Quadratic formulation of disparity estimation problem for light-field camera. In: ICIP (2013)
  24. Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. TPAMI (2014) (to appear)
  25. Yu, Z., Guo, X., Ling, H., Lumsdaine, A., Yu, J.: Line assisted light field triangulation and stereo matching. In: ICCV (2013)
  26. Yu, Z., Yu, J., Lumsdaine, A., Georgiev, T.: An analysis of color demosaicing in plenoptic cameras. In: CVPR (2012)
  
• 作者单位：Computer Vision - ECCV 2014 Workshops
• 丛书名：978-3-319-16180-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper we propose a post-processing pipeline to recover accurately the views (light-field) from the raw data of a plenoptic camera such as Lytro and to estimate disparity maps in a novel way from such a light-field. First, the microlens centers are estimated and then the raw image is demultiplexed without demosaicking it beforehand. Then, we present a new block-matching algorithm to estimate disparities for the mosaicked plenoptic views. Our algorithm exploits at best the configuration given by the plenoptic camera: (i) the views are horizontally and vertically rectified and have the same baseline, and therefore (ii) at each point, the vertical and horizontal disparities are the same. Our strategy of demultiplexing without demosaicking avoids image artifacts due to view cross-talk and helps estimating more accurate disparity maps. Finally, we compare our results with state-of-the-art methods.