Half Quadratic Regularization Model for Image Restoration

DOI

10.2991/msmee-17.2017.221How to use a DOI?

Keywords

Image restoration, half-quadratic, regularization model, Hessian matrix

Abstract

Image restoration easily smears image edge in unsteady area and produces stair effect in steady area. In order to overcome these shortcomings, the paper proposes a half-quadratic energy functional regularization model (EFRM) and structured Newton iterative algorithm. Firstly, for blurred image by system and Gaussian noise, fitting term is described by L2 norm, regularization term is described by half-quadratic function, which can accurately describe image singular property, and the fitting term and regularization term constitute image restoration EFRM. Secondly, resort to Fenchel transform, by introducing auxiliary variables, the primal EFRM is converted into augmented image restoration EFRM. Finally, take advantage of preconditioned theory, Hessian matrix of the transformed model is structured, a new Newton project iterative algorithm is proposed. Comparing to several state-of-art approaches, numerical experiment results show that the proposed EFRM can effectively protect image edge and reduce stair effect, and show better visual effect and higher peak signal-to-noise ratio (PSNR).

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuchao Li
AU  - Songyan Ma
AU  - Yuye Li
PY  - 2017/05
DA  - 2017/05
TI  - Half Quadratic Regularization Model for Image Restoration
BT  - Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)
PB  - Atlantis Press
SP  - 1157
EP  - 1162
SN  - 2352-5401
UR  - https://doi.org/10.2991/msmee-17.2017.221
DO  - 10.2991/msmee-17.2017.221
ID  - Li2017/05
ER  -