Image Smoothing via Truncated Total Variation | Zendy

Zeyang Dou | Zendy; Mengnan Song | Zendy; Kun Gao | Zendy; Zeqiang Jiang | Zendy

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Image Smoothing via Truncated Total Variation

Author(s) -

Zeyang Dou,

Mengnan Song,

Kun Gao,

Zeqiang Jiang

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2773503

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

We present a new regularizer for image smoothing which is particularly effective for diminishing insignificant details, while preserving salient edges. The proposed regularizer relates in spirit to total variation which penalizes all the gradients, while our method just penalizes part of the gradients and leaves the significant edges unchanged. Though the proposed regularizer is a piecewise function, which is hard to optimize, we can unify it to a mathematically sound penalty. The unified penalty term is easy to optimize using recent fast solvers and hard thresholding operation. We show some potential applications of the proposed regularizer, including texture removal and compression artifact restoration. The results show the efficiency of the proposed regularizer.

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