Sparse representations for range data restoration

IEEE Transactions on Image Processing

Volumn 21, Issue 5, 2012, Pages 2909-2915

  Mahmoudi, Mona ^a   Sapiro, Guillermo ^b  

^a Opera Solutions   (United States)

^b University of Minnesota   (United States)

Author keywords

Occlusion restoration; range data denoising; range data resolution enhancement; sparse modeling

Indexed keywords

DE-NOISING; DICTIONARY LEARNING; RANGE DATA; SPARSE REPRESENTATION;

RESTORATION;

THREE DIMENSIONAL;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; LETTER; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84860200348     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2185940     Document Type: Article

References (16)

1. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006.
2. J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration," IEEE Trans. Image Process., vol. 17, no. 1, pp. 53-69, Jan. 2008.
3. W. Dong, X. Li, L. Zhang, and G. Shi, "Sparsity-based image denoising via dictionary learning and structural clustering," in Proc. IEEE Conf. CVPR, 2011, pp. 457-464.
4. O. Schall, A. Belyaev, and H. P. Seidel, "Adaptive feature-preserving non-local denoising of static and time-varying range data," Comput.- Aided Des., vol. 40, no. 6, pp. 701-707, Jun. 2008.
5. M. Mahmoudi and G. Sapiro, Sparse Representations for Three-Dimensional Range Data Restoration. Minneapolis, MN: Univ. Minnesota, 2009, IMA Preprint.
6. I. Tosic, B. Olshausen, and B. Culpepper, "Learning sparse representations of depth," IEEE J. Sel. Topics Signal Process., vol. 5, no. 5, pp. 941-952, Sep. 2011.
7. O. Schall, A. G. Belyaev, and H. P. Seidel, "Robust filtering of noisy scattered point data," in Proc. Eurograph. Symp. Point-Based Graph., 2005, pp. 71-77.
8. O. Rubinstein, Y. Honen, A. M. Bronstein, M. M. Bronstein, and R. Kimmel, "3D color video camera," in Proc. Workshop 3DIM, 2009, pp. 1505-1509.
9. A. Bronstein, M. Bronstein, E. Gordon, and R. Kimmel, High-resolution structured light range scanner with automatic calibration Dept. Comput. Sci., Technion, Haifa, Israel, Tech. Rep. CIS-2003-06, 2003.
10. R. Gal, A. Shamir, and D. Cohen-Or, "Pose-oblivious shape signature," IEEE Trans. Visual. Comput. Graph., vol. 13, no. 2, pp. 261-271, Mar. -Apr. 2007.
11. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," Ann. Statist., vol. 32, no. 2, pp. 407-451, 2004.
12. K. Engan, S. O. Aase, and J. H. Husoy, "Method of optimal directions for frame design," in Proc. IEEE ICASSP, 1999, vol. 5, pp. 2443-2446.
13. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online dictionary learning for sparse coding," in Proc. Int. Conf. Mach. Learn., Jun. 2009, pp. 689-696.
14. S. Mallat and Z. Zhang, "Matching pursuit with time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
15. S. Schuon, C. Theobalt, J. Davis, and S. Thrun, "LidarBoost: Depth superresolution for ToF 3D shape scanning," in Proc. IEEE Int. CVPR, 2009, pp. 343-350.
16. J. Dolson, J. Baek, C. Plagemann, and S. Thrun, "Upsampling range data in dynamic environments," in Proc. IEEE Int. Conf. CVPR, 2010, pp. 1141-1148.