کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9514645 | 1632611 | 2005 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prior Learning and Convex-Concave Regularization of Binary Tomography
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات گسسته و ترکیبات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In our previous work, we introduced a convex-concave regularization approach to the reconstruction of binary objects from few projections within a limited range of angles. A convex reconstruction functional, comprising the projections equations and a smoothness prior, was complemented with a concave penalty term enforcing binary solutions. In the present work we investigate alternatives to the smoothness prior in terms of probabilistically learnt priors encoding local object structure. We show that the difference-of-convex-functions DC-programming framework is flexible enough to cope with this more general model class. Numerical results show that reconstruction becomes feasible under conditions where our previous approach fails.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electronic Notes in Discrete Mathematics - Volume 20, 1 July 2005, Pages 313-327
Journal: Electronic Notes in Discrete Mathematics - Volume 20, 1 July 2005, Pages 313-327
نویسندگان
Stefan Weber, Thomas Schüle, Christoph Schnörr,