SAR image noise suppression of BEMD by the kernel principle component analysis
Abstract In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12015 |
| Summary: | Abstract In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image. |
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| ISSN: | 1751-9659 1751-9667 |