An Adaptive Method for Recovering Image from Mixed Noisy Data

Author:

Liu Jun,Huan Zhongdan,Huang Haiyang,Zhang Haili

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference18 articles.

1. Acar, R., & Vogel, C. (1994). Analysis of total variation penalty methods. Inverse Problems, 10, 1217–1229.

2. Bar, L., Kiryati, N., & Sochen, N. (2006). Image deblurring in the presence of impulsive noise. International Journal of Computer Vision, 70, 279–298.

3. Bect, J., Blanc-Féraud, L., Aubert, J., & Chambolle, A. (2004). A l 1-unified variational framework for image restoration. In Proc. ECCV’2004, Prague, Czech Republic, Part IV: LNCS 3024 (pp. 1–13).

4. Bilmes, J. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Available at http://citeseer.ist.psu.edu/bilmes98gentle.html .

5. Chan, T., & Wong, C. (1998). Total variation blind deconvolution. IEEE Trans. Image Processing, 7, 370–375.

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