Compressed sensing in biological microscopy

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7446, Issue , 2009, Pages

  Marim, Marcio De Moraes ^a,b   Angelini, Elsa D ^b   Olivo Marin, Jean Christophe ^a  

^a INSTITUT PASTEUR   (France)

^b INSTITUT TELECOM   (France)

Author keywords

Compressed sensing; Denoising; Fluorescence microscopy; Multiscale; Photobleaching

Indexed keywords

BIOLOGICAL APPLICATIONS; BIOLOGICAL FLUORESCENCE; BIOLOGICAL MICROSCOPY; COMPRESSED SENSING; DE-NOISE; DENOISING; DENOISING METHODS; EXCITATION LIGHT; EXPERIMENTAL OPTIMIZATION; EXPOSURE-TIME; FLUORESCENCE IMAGE; FLUORESCENT MOLECULES; IMAGE DE-NOISING; IMAGING PROTOCOL; LOW SNR; MICROSCOPY IMAGES; MULTISCALE; MULTISCALE FRAMEWORK; NOISE LEVELS; NOISE RECONSTRUCTION; NOVEL TECHNIQUES; OPTIMALITY; POST-PROCESSING TECHNIQUES; SIGNAL MODELING; STATISTICAL PROPERTIES;

ACOUSTIC INTENSITY; FLUORESCENCE; FLUORESCENCE MICROSCOPY; IMAGE PROCESSING; PHOTOBLEACHING; SIGNAL RECONSTRUCTION; SIGNAL TO NOISE RATIO;

IMAGE RECONSTRUCTION;

EID: 70449488328     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.826275     Document Type: Conference Paper

References (25)

1. A. Buades, B. Coll, and J. Morel, "A review of image denoising algorithms, with a new one, " Multiscale Modeling and Simulation (SIAM interdisciplinary journal) 4 (2), pp. 490-530, 2005.
2. L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear Total Variation based noise removal algorithms, " Physica D 60, pp. 259-268, 1992.
3. J. Weickert, B. T. H. Romeny, and M. Viergever, "Efficient and reliable schemes for nonlinear diffusion filtering, " IEEE Trans. Image Processing 7, pp. 398-410, 1998.
4. R. DeVore and B. Lucier, "Fast wavelet techniques for near-optimal processing, " IEEE Military Communications Conference, New York, pp. 48.3.1-48.3.7, 1992.
5. D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage, " Journal of the American Statistical Association 90, pp. 1200-1224, 1995.
6. M. Elad and M. Aharon, "Image denoising via learned dictionaries and sparse representation, " Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR), New-York, June 17-22 2006.
7. M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD algorithm, " Proceedings of SPARSE'05, Rennes, France, november 2005.
8. A. Hyvärinen, P. Hoyer, and E. Oja, "Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation, " Advances in Neural Information Processing Systems NIPS 11, pp. 473-479, 1999.
9. M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging, " Magnetic Resonance in Medicine 58(6), pp. 1182-1195, December 2007.
10. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, " IEEE Trans. on Information Theory 52(2), pp. 489-509, June 2006.
11. E. Candès and J. Romberg, "Practical signal recovery from random projections, " in Proceedings of the SPIE Conference on Wavelet Applications in Signal and Image Processing XI, In SPIE International Symposium on Electronic Imaging: Computational Imaging III, p. 5914, (San Jose, California), January 2005.
12. D. L. Donoho, "Compressed sensing, " IEEE Trans. on Information Theory 52(4), pp. 1289-1306, April 2006.
13. D. Donoho, M. Elad, and V. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise, " IEEE Transactions on Information Theory 52, pp. 6-18, January 2006.
14. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by Basis Pursuit, " SIAM Journal on Scientific Computing 20, pp. 33-61, February 1998.
15. D. L. Donoho, "Sparse components of images and optimal atomic decompositions, " Constructive Approximation 17, pp. 353-382, January 2001.
16. E. Candès and J. Romberg, "Sparsity and incoherence in Compressive Sampling, " Inverse Problems 23(3), pp. 969-985, November 2006.
17. 1, " American Journal of Mathematics 121, pp. 587-628, 1999.
18. T. F. Chan, S. Osher, and J. Shen, "The digital TV filter and nonlinear denoising, " IEEE Transactions on Image Processing 10(2), pp. 231-241, February 2001.
19. E. Candès, "The restricted isometry property and its implications for Compressed Sensing, " Compte Rendus de l'Academie des Sciences, Paris Serie I, pp. 346589-592, February 2008.
20. E. Candès and T. Tao, "Decoding by linear programming, " IEEE Transactions on Information Theory 51(12), pp. 4203-4215, 2005.
21. E. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements, " Communications on Pure and Applied Mathematics 59(8), pp. 1207-1223, August 2006.
22. T. Lindeberg, Scale-Space Theory in Computer Vision, The Kluwer International Series in Engineering and Computer Science, Dordrecht: Kluwer Academic, 1994.
23. C. Rose and M. D. Smith, Mathematical Statistics with Mathematica, chapter 7.2C: k-Statistics: Unbiased Estimators of Cumulants, Springer-Verlag, 2002.
24. M. Marim, E. D. Angelini, and J.-C. Olivo-Marin, "A Compressed Sensing approach for biological microscopic image processing, " IEEE International Symposium on Biomedical Imaging: From Nano to Macro - ISBI, pp. 1374-1377, 2009.
25. G. Gilboa, N. Sochen, and Y. Y. Zeevi, "Texture preserving variational denoising using an adaptive fidelity term, " IEEE Trans. on Image Processing 15(8), pp. 2281-2289, 2006.