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5月7日学术报告2(西安电子科大 董伟生： Image Restoration via Simultaneous Spa
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类别：网络整理
发布人：admin
浏览次数： 次
发布时间：2015-09-02 14:24
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题目： Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture
时间： 2015年5月7日 9:00-10:00
地点：外围投注365B-404室
报告人：董伟生 副教授
报告人国籍： 中国
报告人简介：董伟生2004年在华中科技大学获通信工程学士学位， 2010年在西安电子科技大学获电路域系统工学博士学位。从2009年1月到2010年6月香港理工大学计算学系研究助理，2012-2014年微软亚洲研究院视觉计算组访问学者。现任西安电子科技大学副教授。在图像处理领域发表30余篇论文，其中两篇排名ESI前0.1%。在SPIE VCIP 2010会议上获最佳论文奖。
报告摘要：
In image processing, sparse coding has been known to be relevant to both variational and Bayesian approaches. The regularization
parameter in variational image restoration is intrinsically connected with the shape parameter of sparse coefficients’ distribution
in Bayesian methods. How to set those parameters in a principled yet spatially adaptive fashion turns out to be a challenging
problem especially for the class of nonlocal image models. In this talk, I will propose a structured sparse coding framework to
address this issue—more specifically, a nonlocal extension of Gaussian scale mixture (GSM) model is developed using simultaneous sparse coding (SSC) and its applications into image restoration are explored. It is shown that the variances of
sparse coefficients (the field of scalar multipliers of Gaussians)—if treated as a latent variable—can be jointly estimated along with the unknown sparse coefficients via the method of alternating optimization. When applied to image restoration,
our experimental results have shown that the proposed SSC–GSM technique can both preserve the sharpness of edges and suppress undesirable artifacts. Thanks to its capability of achieving a better spatial adaptation, SSC–GSM based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing
approaches.
邀请人： 胡瑞敏 教授
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