tailieunhanh - Báo cáo hóa học: " Research Article A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 34821 21 pages doi 2007 34821 Research Article A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization V. Patanavijit and S. Jitapunkul Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand Received 31 August 2006 Revised 12 March 2007 Accepted 16 April 2007 Recommended by Richard R. Schultz Recently there has been a great deal of work developing super-resolution reconstruction SRR algorithms. While many such algorithms have been proposed the almost SRR estimations are based on L1 or L2 statistical norm estimation therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence are unknown consequently SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper first comprehensively reviews the SRR algorithms in this last decade and addresses their shortcomings and latter proposes a novel robust SRR algorithm that can be applied on several noise models. The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. For removing outliers in the data the Lorentzian error norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image. Moreover Tikhonov regularization and Lorentzian-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 .