References :

[1] A. Buades, B. Coll, and J. M. Morel, “A Review of Image Denoising Methods, with a New One,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005.
[2] S. Roth and M. J. Black, “Fields of experts,” International Journal of Computer Vision (IJCV), vol. 82, no. 2, pp. 205–229, April 2009.
[3] M. Elad and M. Aharon, “Image Denoising via Sparse and Redundant Representations over Learned Dictionaries,” IEEE Trans. on Image Processing, vol. 15, no. 12, pp. 3736–3745, December 2006.
[4]K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Processing, vol. 16, no. 8, pp. 2080–2095, August 2007.
[5]J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-Local Sparse Models for Image Restoration," Proc. of ICCV, September-October 2009.
[6]C. Liu, R. Szeliski, S. B. Kang, C. L. Zitnick, and W. T. Freeman, “Automatic Estimation and Removal of Noise from a Single Image,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 299–314, February 2008. (1st Order results shown)
[7]N. Joshi, C. Zitnick, R. Szeliski, and D. Kriegman, “Image deblurring and denoising using color priors,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida, June 2009.
[8] Neat Image: Commercial denoising software (http://www.neatimage.com)