Automated parameter selection in the L1-L2-TV model for removing Gaussian plus impulse noise

A. Langer

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The minimization of a functional consisting of a combined L1/L2-data-fidelity term and a total variation term, named L1-L2-TV model, is considered to remove a mixture of Gaussian and impulse noise in images, which are possibly additionally deformed by some convolution operator. We investigate analytically the stability of this model with respect to its parameters and link it to a constrained minimization problem. Based on these investigations and a statistical characterization of the mixed Gaussian-impulse noise a fully automated parameter selection algorithm for the L1-L2-TV model is presented. It is shown by numerical experiments that the proposed method finds parameters with which noise is removed considerably while features are preserved in images.
Original languageEnglish
Article number074002
JournalInverse Problems
Issue number7
Publication statusPublished - 21 Jun 2017
Externally publishedYes

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