Research on UAV image denoising effect based on improved Wavelet Threshold of BEMD
Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained aft...
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| Vydané v: | Journal of physics. Conference series Ročník 1437; číslo 1; s. 12032 - 12043 |
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01.01.2020
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained after BEMD decomposition of noisy image, and then the high frequency apply particle swarm optimization (PSO) after wavelet decomposition to the high-frequency IMF to obtain the optimal threshold filtering. As for the low frequency, it is filtered by the exponential attenuation threshold algorithm of wavelet semi-soft threshold. Finally, the filtered both high-frequency and low-frequency IMF are reconstructed after inverse wavelet transformation. This algorithm is applied to the processing of UAV images, and compared with the traditional UAV image denoising method and the advanced UAV image denoising method. Simulation results demonstrate that the proposed denoising method outperforms other de-noising methods in terms of peak signal-to-noise ratio and mean square error. |
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| AbstractList | Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained after BEMD decomposition of noisy image, and then the high frequency apply particle swarm optimization (PSO) after wavelet decomposition to the high-frequency IMF to obtain the optimal threshold filtering. As for the low frequency, it is filtered by the exponential attenuation threshold algorithm of wavelet semi-soft threshold. Finally, the filtered both high-frequency and low-frequency IMF are reconstructed after inverse wavelet transformation. This algorithm is applied to the processing of UAV images, and compared with the traditional UAV image denoising method and the advanced UAV image denoising method. Simulation results demonstrate that the proposed denoising method outperforms other de-noising methods in terms of peak signal-to-noise ratio and mean square error. |
| Author | Pingjuan, NIU Xueru, MA Run, MAO She, Huanlin Hao, Shi Pan, Jie Wang, Shan |
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| Cites_doi | 10.1098/rspa.1998.0193 10.3233/BME-141143 10.1109/18.382009 10.1587/transinf.2016EDP7399 10.1109/TIP.2013.2277813 10.3390/app8101977 10.1016/S1874-1029(11)60205-X |
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| SubjectTerms | Algorithms Decomposition Noise reduction Particle swarm optimization Physics Sharpness Signal to noise ratio Unmanned aerial vehicles Wave attenuation Wavelet transforms |
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