References

Addesso, P., R. Conte, M. Longo, R. Restaino and G. Vivone, 2012. MAP-MRF cloud detection based on PHD filtering. Sel. Top. Appl. Earth Obs. Remote Sens. IEEE. J., 5: 919-929.
CrossRef  |  Direct Link  |  

Al-Amri, S.S. and N.V. Kalyankar, 2010. A comparative study for deblured average blurred images. Int. J. Comput. Sci. Eng., 2: 731-733.
Direct Link  |  

Bojarczak, P. and Z. Lukasik, 2007. Image deblurring-wiener filter versus TSVD approach. Adv. Electr. Electron. Eng., 6: 86-89.
Direct Link  |  

Bruzzone, L. and D.F. Prieto, 2000. Automatic analysis of the difference image for unsupervised change detection. Geosci. Remote Sens. IEEE. Trans., 38: 1171-1182.
CrossRef  |  PubMed  |  Direct Link  |  

Bruzzone, L. and D.F. Prieto, 2002. An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. Image Process. IEEE. Trans., 11: 452-466.
CrossRef  |  PubMed  |  Direct Link  |  

Buades, A., B. Coll and J.M. Morel, 2005. A non-local algorithm for image denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 20-26 IEEE Computer Society, USA., pp: 60-65.

Cai, J.F., H. Ji, C. Liu and Z. Shen, 2009. Blind motion deblurring from a single image using sparse approximation. Proceedings of the Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL., pp: 104-111.

Cai, J.F., H. Ji, C. Liu and Z. Shen, 2012. Framelet-based blind motion deblurring from a single image. Image Process. IEEE. Trans., 21: 562-572.
CrossRef  |  PubMed  |  Direct Link  |  

Deng, H. and D.A. Clausi, 2004. Unsupervised image segmentation using a simple MRF model with a new implementation scheme. Pattern Recognit., 37: 2323-2335.
CrossRef  |  Direct Link  |  

Dey, N., L.B. Feraud, C. Zimmer, P. Roux and Z. Kam et al., 2006. Richardson-lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc. Res. Tech., 69: 260-266.
CrossRef  |  PubMed  |  Direct Link  |  

Kasetkasem, T. and P.K. Varshney, 2002. An image change detection algorithm based on Markov random field models. Geosci. Remote Sens. IEEE. Trans., 40: 1815-1823.
CrossRef  |  Direct Link  |  

Li, S.Z., 2009. Markov Random Field Modeling in Image Analysis. 3rd Edn., Springer, Heidelberg, ISBN: 9781848002784, pp: 74-76.

Moser, G. and S.B. Serpico, 2009. Unsupervised change detection from multichannel SAR data by Markovian data fusion. Geosci. Remote Sens. IEEE. Trans., 47: 2114-2128.
CrossRef  |  Direct Link  |  

Moser, G., S. Serpico and G. Vernazza, 2007. Unsupervised change detection from multichannel SAR images. Geosci. Remote Sens. Lett. IEEE., 4: 278-282.
CrossRef  |  Direct Link  |  

Niu, X. and Y. Ban, 2012. An adaptive contextual SEM algorithm for urban land cover mapping using multitemporal high-resolution polarimetric SAR data. Appl. Earth Obs. Remote Sens. IEEE. J., 5: 1129-1139.
CrossRef  |  Direct Link  |  

Sankhe, P.D., M. Patil and M. Margaret, 2011. Deblurring of grayscale images using inverse and Wiener filter. Proceedings of the International Conference and Workshop on Emerging Trends in Technology, February 25-26, 2011, ACM, Mumbai, Maharashtra, India, ISBN: 978-1-4503-0449-8, pp: 145-148.

Sharma, S., S. Sharma and R. Mehra, 2013. Image restoration using modified lucy richardson algorithm in the presence of Gaussian and Motion Blur. Adv. Electron. Electr. Eng., 3: 1063-1070.
Direct Link  |  

Singh, M.K., U.S. Tiwary and Y.H. Kim, 2008. An adaptively accelerated lucy-richardson method for image deblurring. EURASIP. J. Adv. Signal Process., 365021: 1-10.
CrossRef  |  Direct Link  |  

Tekalp, A.M., M.K. Ozkan and M.I. Sezan, 1992. High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration. Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP-92., 1992, March 23-26, 1992, IEEE, San Francisco, CA., pp: 169-172.

Tso, B.C. and P.M. Mather, 1999. Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields. Geosci. Remote Sens. IEEE. Trans., 37: 1255-1260.
CrossRef  |  Direct Link  |  

Xu, M., H. Chen and P.K. Varshney, 2011. An image fusion approach based on Markov random fields. Geosci. Remote Sens. IEEE. Trans., 49: 5116-5127.
CrossRef  |  Direct Link  |  

Yang, X. and D.A. Clausi, 2012. Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs. Sel. Top. Appl. Earth Obs. Remote Sens. IEEE. J., 5: 1383-1393.
CrossRef  |  Direct Link  |  

You, D., S. Antani, D.D. Fushman and G.R. Thoma, 2014. An MRF model for biomedical image segmentation. Proceeding of the IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), 2014, May 27-29, 2014, IEEE, New York, USA., pp: 539-540.

Zheng, C., L. Wang, Y. Hu and Q. Qin, 2011. Region-based MRF model with optimized initial regions for image segmentation. Proceeding of the 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE), June 24-26, 2011, IEEE, Nanjing, China, pp: 3354-3357.