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- W2600457519 abstract "Variational methods have become an important kind of methods in signal and image restoration-a typical inverse problem. One important minimization model consists of the squared ℓ2 data fidelity (corresponding to Gaussian noise) and a regularization term constructed by a potential function composed of first order difference operators. It is well known that total variation (TV) regularization, although achieved great successes, suffers from a contrast reduction effect. Using a typical signal, we show that, actually all convex regularizers and most nonconvex regularizers have this effect. With this motivation, we present a general truncated regularization framework. The potential function is a truncation of existing nonsmooth potential functions and thus flat on (τ,+∞ for some positive τ. Some analysis in 1D theoretically demonstrate the good contrast-preserving ability of the framework. We also give optimization algorithms with convergence verification in 2D, where global minimizers of each subproblem (either convex or nonconvex) are calculated. Experiments numerically show the advantages of the framework." @default.
- W2600457519 created "2017-04-07" @default.
- W2600457519 creator A5010997999 @default.
- W2600457519 creator A5026163142 @default.
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- W2600457519 date "2018-07-03" @default.
- W2600457519 modified "2023-09-26" @default.
- W2600457519 title "A general truncated regularization framework for contrast-preserving variational signal and image restoration: Motivation and implementation" @default.
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- W2600457519 doi "https://doi.org/10.1007/s11425-017-9260-8" @default.
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