Fractal Dimension based SAR Image Sparse Degrees Estimation

DOI

10.2991/iceeecs-16.2016.115How to use a DOI?

Keywords

Compressive sensing, Measurement matrix, Sparsity, Fractal dimension, SAR image

Abstract

Compressed sensing is surprisingly predicts that images, which allow a sparse representation by a suitable basis or a frame, can be recovered from what was previously considered highly incomplete linear measurements by using efficient algorithms. It is a novel research area of SAR images processing. As a result of the design of the observation matrix estimate depends on the image sparse degree, therefore, the estimation precision of the image sparse degree is an important factor to whether can accurate reconstruction images. In this paper, the relationship between the degrees of SAR image sparse and fractal dimension is researched. The experimental results show that the SAR image fractal dimension is proportional to the sizes of the observation matrix, which can be used to set the parameters of the observation matrix.

Copyright

© 2016, 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  - Hua Bo
AU  - Haiyun Gu
AU  - Lei Ren
AU  - Hong Xie
PY  - 2016/12
DA  - 2016/12
TI  - Fractal Dimension based SAR Image Sparse Degrees Estimation
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 573
EP  - 578
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.115
DO  - 10.2991/iceeecs-16.2016.115
ID  - Bo2016/12
ER  -