Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization
KAUST Grant NumberKUK-I1-007-43
Online Publication Date2013-11-27
Print Publication Date2013
Permanent link to this recordhttp://hdl.handle.net/10754/598555
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AbstractWe propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.
CitationReyes JCD los, Schönlieb C-B (2013) Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization. IPI 7: 1183–1214. Available: http://dx.doi.org/10.3934/ipi.2013.7.1183.
SponsorsResearch partially supported by the Alexander von Humboldt Foundation. CBS acknowledges the financial support provided by the Cambridge Centre for Analysis (CCA), the Royal Society International Exchanges Award IE110314 for the project High-order Compressed Sensing for Medical Imaging, the EPSRC first grant Nr. EP/J009539/1 Sparse & Higher-order Image Restoration, and the EPSRC / Isaac Newton Trust Small Grant on Non-smooth geometric reconstruction for highresolution MRI imaging of fluid transport in bed reactors. Further, this publication is based on work supported by Award No. KUK-I1-007-43, made by King Abdullah University of Science and Technology (KAUST).
JournalInverse Problems and Imaging