Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain
We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify t...
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Hindawi Limited
2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/493142 |
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doaj-0b3ffc231ccb4f5281155167974261db2020-11-25T00:59:00ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/493142493142Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform DomainYan Zhou0Qingwu Li1Guanying Huo2Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, ChinaKey Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, ChinaKey Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, ChinaWe propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms.http://dx.doi.org/10.1155/2015/493142 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yan Zhou Qingwu Li Guanying Huo |
spellingShingle |
Yan Zhou Qingwu Li Guanying Huo Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain Mathematical Problems in Engineering |
author_facet |
Yan Zhou Qingwu Li Guanying Huo |
author_sort |
Yan Zhou |
title |
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain |
title_short |
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain |
title_full |
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain |
title_fullStr |
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain |
title_full_unstemmed |
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain |
title_sort |
automatic side-scan sonar image enhancement in curvelet transform domain |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms. |
url |
http://dx.doi.org/10.1155/2015/493142 |
work_keys_str_mv |
AT yanzhou automaticsidescansonarimageenhancementincurvelettransformdomain AT qingwuli automaticsidescansonarimageenhancementincurvelettransformdomain AT guanyinghuo automaticsidescansonarimageenhancementincurvelettransformdomain |
_version_ |
1725219344942628864 |