Fast and flexible convolutional sparse coding

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 5135-5143

  Heide, Felix ^a   Heidrich, Wolfgang ^b   Wetzstein, Gordon ^a  

^a STANFORD UNIVERSITY   (United States)

^b KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; CONVOLUTION; IMAGE CODING; LEARNING SYSTEMS; PATTERN RECOGNITION;

FEATURE LEARNING; IMAGE FEATURES; INCOMPLETE DATA; LOCAL NEIGHBORHOODS; NEW APPROACHES; PATCH-BASED METHODS; RECONSTRUCTION PROBLEMS; STATE OF THE ART;

CODES (SYMBOLS);

EID: 84959235606     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299149     Document Type: Conference Paper

References (24)

1. M. S. Almeida and M. A. Figueiredo. Frame-based image deblurring with unknown boundary conditions using the alternating direction method of multipliers. In Proc. ICIP, pages 582-585, 2013.
2. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2011.
3. H. Bristow, A. Eriksson, and S. Lucey. Fast convolutional sparse coding. In Proc. CVPR, pages 391-398, 2013.
4. H. Bristow and S. Lucey. Optimization methods for convolutional sparse coding. arXiv:1406. 2407, 2014.
5. A. M. Bruckstein, D. L. Donoho, and M. Elad. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM review, 51(1):34-81, 2009.
6. V. Cevher, S. Becker, and M. Schmidt. Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Processing Magazine, 31(5):32-43, 2014.
7. B. Chen, G. Polatkan, G. Sapiro, D. Blei, D. Dunson, and L. Carin. Deep learning with hierarchical convolutional factor analysis. IEEE Trans. PAMI, 35(8):1887-1901, 2013.
8. W. Dong, G. Shi, X. Li, Y. Ma, and F. Huang. Compressive sensing via nonlocal low-rank regularization. IEEE Transac-tions on Image Processing, 23(8):3618-3632, 2014.
9. R. B. Grosse, R. Raina, H. Kwong, and A. Y. Ng. Shiftinvariance sparse coding for audio classification. In Proc. UAI, pages 149-158, 2007.
10. F. Heide, M. Rouf, M. B. Hullin, B. Labitzke, W. Heidrich, and A. Kolb. High-quality computational imaging through simple lenses. ACM Trans. Graph., 32(5):149, 2013.
11. F. Heide, L. Xiao, A. Kolb, M. B. Hullin, and W. Heidrich. Imaging in scattering media using correlation image sensors and sparse convolutional coding. OSA Opt. Exp., 22(21):26338-26350, Oct 2014.
12. X. Hu, Y. Deng, X. Lin, J. Suo, Q. Dai, C. Barsi, and R. Raskar. Robust and accurate transient light transport decomposition via convolutional sparse coding. OSA Opt. Lett., 39(11):3177-3180, 2014.
13. K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu, and Y. LeCun. Learning convolutional feature hierachies for visual recognition. In Proc. NIPS, 2010.
14. B. Kong and C. C. Fowlkes. Fast Convolutional Sparse Coding (FCSC). Technical report, UCI, May 2014.
15. X. Lin, G. Wetzstein, Y. Liu, and Q. Dai. Dual-coded compressive hyperspectral imaging. OSA Opt. Lett., 39(7):2044-2047, 2014.
16. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proc. ICML, pages 689-696. ACM, 2009.
17. K. Marwah, G. Wetzstein, Y. Bando, and R. Raskar. Compressive light field photography using overcomplete dictionaries and optimized projections. ACM Trans. Graph. (SIG-GRAPH), 32(4):46:1-46:12, 2013.
18. B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision Re-search, 37(23):3311-3325, 1997.
19. N. Parikh and S. Boyd. Proximal algorithms. Foundations and Trends in Optimization, 1(3):123-231, 2013.
20. M. Schmidt, N. L. Roux, and F. Bach. Minimizing finite sums with the stochastic average gradient. arXiv:1309. 2388, 2013.
21. A. Szlam, K. Kavukcuoglu, and Y. LeCun. Convolutional matching pursuit and dictionary training. arXiv:1010. 0422, 2010.
22. M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus. Deconvolutional networks. In Proc. CVPR, pages 2528-2535, 2010.
23. M. D. Zeiler, G. W. Taylor, and R. Fergus. Adaptive deconvolutional networks for mid and high level feature learning. In Proc. ICCV, pages 2018-2025, 2011.
24. D. Zoran and Y. Weiss. From learning models of natural image patches to whole image restoration. In Proc. ICCV, pages 479-486, 2011.