Denoising with higher order derivatives of bounded variation and an application to parameter estimation
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Regularization with functions of bounded variation has been proven to be effective for denoising signals and images. This nonlinear regularization technique, in contrast with linear regularization techniques like Tikhonov regularization, has the advantage that discontinuities in signals and images can be located very precisely. In this paper bounded variation regularization is generalized to functions with higher order derivatives of bounded variation. This concept is applied to locate discontinuities in derivatives, which has important applications in parameter estimation problems.
AMS Subject Classifications65J10 65J15 65J20 65K10 26A45
Key wordsNondifferentiable optimization problems nonreflexive spaces regularization bounded variation norm and generalizations
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