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ICCV
2005
IEEE

Mutual Information Regularized Bayesian Framework for Multiple Image Restoration

14 years 5 months ago
Mutual Information Regularized Bayesian Framework for Multiple Image Restoration
to appear in Proc. IEEE International Conference on Computer Vision (ICCV), 2005 Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity. In the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative sig...
Yunqiang Chen, Hongcheng Wang, Tong Fang, Jason Ty
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Yunqiang Chen, Hongcheng Wang, Tong Fang, Jason Tyan
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