Research of Improved Generalized Iteration Shrinkage Algorithm

DOI

10.2991/meees-18.2018.35How to use a DOI?

Keywords

generalized iterative shrinkage algorithm, regularization, image restoration, gradient acceleration operator.

Abstract

Generalized iterative shrinking algorithm combined with the contraction operator and the gradient operator of the fitting term can easily solve the regularized non-convex optimization problem and has a very good ability of image restoration. However, the gradient descent speed in the generalized iterative shrinking algorithm is slow, which limits the convergence rate of the algorithm. To solve this problem, a Nesterov gradient acceleration operator is introduced and an improved generalized iterative shrinkage algorithm is proposed. The improved algorithm accelerates the gradient descent in each iteration, speeding up the convergence of the algorithm. Experimental results show that compared with the generalized iterative contraction algorithm, the improved algorithm has a great improvement in visual effects and peak signal-to-noise ratio. And it has a faster convergence speed.

Copyright

© 2018, 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/).

Download article (PDF)

TY  - CONF
AU  - Zhicheng Guo
PY  - 2018/05
DA  - 2018/05
TI  - Research of Improved Generalized Iteration Shrinkage Algorithm
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 191
EP  - 195
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.35
DO  - 10.2991/meees-18.2018.35
ID  - Guo2018/05
ER  -