Author Affiliations
1College of Earth Sciences, Guilin University of Technology, Guilin 541004, China2Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, Chinashow less
Fig. 1. Denoised results for the synthetic ground penetrating radar (GPR) data with random and coherent noise using traditional curvelet transform: (a) Original GPR data; (b) data with random noise, PSNR = 17 dB; (c) data with coherent noise, PSNR = 15.51 dB; (d) the δ value vs. PSNR value curves
含噪声模拟合成探地雷达数据的曲波变换阈值去噪结果 (a)原始合成数据; (b)含随机噪声数据, PSNR = 17 dB; (c)含相关噪声数据, PSNR = 15.51 dB; (d)去噪数据PSNR值随阈值控制系数δ的变化
Fig. 2. Denoised results for the synthetic GPR data (Fig. 1. (b) and Fig. 1. (c)) with δ = 0.08 using traditional curvelet transform: (a) Result for random noise, PSNR = 20.23 dB; (b) result for coherent noise, PSNR = 16.75 dB.
δ = 0.08时含噪声数据(图1(b)和图1(c))曲波变换去噪结果 (a)随机噪声数据去噪结果, PSNR = 20.23 dB, 提高3.23 dB; (b)相关噪声数据去噪结果, PSNR = 16.75 dB, 提高1.24 dB
Fig. 3. Denoised results for the synthetic GPR data (Fig. 1. (b) and Fig. 1. (c)) using curvelet transform with statistical self-adaption: (a) Result for random noise, PSNR = 25.3 dB; (b) result for coherent noise, PSNR = 21.92 dB.
含噪声数据(图1(b)和图1(c))的统计量自适应阈值曲波变换去噪结果 (a)随机噪声数据去噪结果, PSNR = 25.3 dB, 提高8.3 dB; (b)相关噪声数据去噪结果, PSNR = 21.92 dB, 提高6.41 dB
Fig. 4. Denoised results for the synthetic GPR data of complex model with random and coherent noise using curvelet transform with statistical self-adaption: (a) Original GPR data; (b) data with random noise, PSNR = 16.04 dB; (c) data with coherent noise, PSNR = 15.7 dB; (d) result for random noise, PSNR = 23.97 dB; (e) result for coherent noise, PSNR = 21.05 dB.
双矩形目标模型含噪声数据统计量自适应阈值曲波变换去噪结果 (a)原始合成数据; (b)含随机噪声数据, PSNR = 16.04 dB; (c)含相关噪声数据, PSNR = 15.7 dB; (d)随机噪声数据去噪结果, PSNR = 23.97 dB; (e)相关噪声数据去噪结果, PSNR = 21.05 dB
Fig. 5. Denoised results for the synthetic GPR data (Fig. 4.) in the 50th receiver using curvelet transform with statistical self-adaption: (a) Result for random noise; (b) result for coherent noise.
双矩形目标模型含噪声数据第50道统计量自适应阈值曲波变换去噪结果 (a)随机噪声数据去噪结果; (b)相关噪声数据去噪结果
Fig. 6. Denoised results for field GPR data using curvelet transform with L2 standard deviation and statistical self-adaption respectively: (a) Original field GPR data; (b) result using curvelet transform with L2 standard deviation; (c) result using curvelet transform with statistical self-adaption.实测探地雷达时间剖面传统曲波变换去噪和本文去噪算法处理结果 (a)实测时间剖面; (b)采用L2标准方差估计阈值曲波去噪结果; (c)统计量自适应阈值曲波去噪结果
Fig. 7. Denoised results for field GPR data (Fig. 6) in the 200th receiver using curvelet transform with L2 standard deviation and statistical self-adaption respectively.
实测探地雷达时间剖面第200道传统曲波变换去噪和本文去噪算法处理结果