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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ying Li Hongli Gong Qing Wang |
| Copyright Year | 2010 |
| Abstract | Speckle and low contrast can cause image degradation, which reduces the detectability of targets and impedes further investigation of synthetic aperture radar (SAR) images. This paper presents an adaptive enhancement method with speckle reduction for SAR images using mirror-extended curve let (ME-curve let) transform and particle swarm optimization (PSO). First, an improved enhancement function is proposed to nonlinearly shrink and stretch the curve let coefficients. Then, a novel objective evaluation criterion is introduced to adaptively obtain the optimal parameters in the enhancement function. Finally, a PSO algorithm with two improvements is used as a global search strategy for the best enhanced image. Experimental results indicate that the proposed method can reduce the speckle and enhance the edge features and the contrast of SAR images better with comparison to the wavelet-based and curve let-based non-adaptive enhancement methods. |
| Starting Page | 4520 |
| Ending Page | 4523 |
| File Size | 551484 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.1098 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Transforms Image edge detection Noise Speckle Convergence Synthetic aperture radar Particle swarm optimization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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