First Authors | Yuanhao Gong |
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Authors | Yuanhao Gong, Ivo F. Sbalzarini |
Corresponding Authors | |
Last Authors | Ivo F. Sbalzarini |
Journal Name | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process) |
Volume | 26 |
Issue | 4 |
Page Range | 1786-1798 |
Open Access | true |
Print Publication Date | 2017-04-01 |
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Abstract | In image processing, the rapid approximate solution of variational problems involving generic data-fitting terms is often of practical relevance, for example in real-time applications. Variational solvers based on diffusion schemes or the Euler-Lagrange equations are too slow and restricted in the types of data-fitting terms. Here, we present a filter-based approach to reduce variational energies that contain generic data-fitting terms, but are restricted to specific regularizations. Our approach is based on reducing the regularization part of the variational energy, while guaranteeing non-increasing total energy. This is applicable to regularization-dominated models, where the data-fitting energy initially increases, while the regularization energy initially decreases. We present fast discrete filters for regularizers based on Gaussian curvature, mean curvature, and total variation. These pixel-local filters can be used to rapidly reduce the energy of the full model. We prove the convergence of the resulting iterative scheme in a greedy sense, and we show several experiments to demonstrate applications in image-processing problems involving regularization-dominated variational models. |
Gong_2017_6799.pdf (7.4 MB) | |
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Affiliated With | Predoc first author, Sbalzarini, CSBD |
Selected By | Sbalzarini |
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Publication Status | Published |
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DOI | 10.1109/TIP.2017.2658954 |
PubMed ID | 28141519 |
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Created By | sbalzari |
Added Date | 2017-03-07 |
Last Edited By | herbst |
Last Edited Date | 2021-05-12 16:26:05.319 |
Library ID | 6799 |
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