A New Image Restoration Algorithm Based on Mathematical Morphology and Wavelet Neural Network

DOI

10.2991/iccasm.2012.263How to use a DOI?

Keywords

Sea clutter, Wavelet neural network, Amoeba structure element, Image restoration

Abstract

Wavelet neural network (WNN) is introduced into the field of image restoration due to the excellent local feature and adaptive ability. The procedure of restoration can be looked as an approximating procedure from noisy image to original image. The better WNN has approximation performance, the better restoration performance. With the help of wavelet neural network and mathematical morphology, a new image restoration algorithm is proposed. It can effectively maintain the image edges and details. In order to overcome the shortcomings of the filter with fixed structure, amoeba structure element is presented based on mathematical morphology. Image data is extracted by amoeba structure element, and input into the wavelet neural network .The WWN is trained by BP algorithm in batch mode training, which adjusts the wavelet coefficient and network weights adaptively.The experimental results showed that the approach proposed can preserve fine details and excellent fidelity and is better than general denoising methods.

Copyright

© 2012, 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  - Yan Shen
AU  - Ming Liu
AU  - Jinbao Wang
AU  - Wenlu Zhou
PY  - 2012/08
DA  - 2012/08
TI  - A New Image Restoration Algorithm Based on Mathematical Morphology and Wavelet Neural Network
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1036
EP  - 1039
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.263
DO  - 10.2991/iccasm.2012.263
ID  - Shen2012/08
ER  -