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Event(s) on February 2011


  • Monday, 14th February, 2011

    Title: DLS: Optimization Techniques to Deal with Lack of Data in Statistical Estimation
    Speaker: Prof. Roger J-B Wets, University of California, Davis, USA
    Time/Place: 11:00  -  12:00 (Preceded by Reception at 10:30am)
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: To describe the stochastic environment in descriptive or prescriptive models, it is implicitly assumed that enough data will be available to guarantee the validity of a decision or the consistency of a statistical estimate. Unfortunately, in a real life environment the data available is rarely enough to reach the asymptotic range, either because it is not available or there is not enough time to collect an adequate data base before decisions or estimates must be produced. One serious shortcoming is our ability to systematically blend data and non-data information, in other words, our inability to deal with the fusion of hard and soft information. The lecture deals with these challenges.


  • Wednesday, 23rd February, 2011

    Title: Fast Minimization methods for Multiplicative Noise Removal
    Speaker: Mr. WANG Fan, Hong Kong Baptist University, Hong Kong
    Time/Place: 10:30  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover the blurred and noisy image. We make use of the logarithm to transform multiplicative problems into additive problems and then employ l1-norm to measure the data-fitting. The total variation is also used as a regularization to the recovered image. We use the alternating direction methods (ADM) to handle the optimization model. As the set of feasible solutions is nonconvex in the formulation, we propose to use approximation to make it to be convex, and therefore make sure the convergence of the proposed algorithm. Experimental results are report to demonstrate that the proposed algorithm performs better than the other existing methods.

 

 


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