Separate removal of random noise and clutter in GPR images based on Self2Self and NSST

References

• Aboudourib, A., M. Serhir, and D. Lesselier. 2020. ”A Processing Framework for Tree-Root Reconstruction Using Ground-Penetrating Radar Under Heterogeneous Soil Conditions.” IEEE Transactions on Geoscience and Remote Sensing 59 (1): 208–219. IEEE. doi:10.1109/TGRS.2020.2993719.
   Web of Science ®Google Scholar
• Anderson, J. M. M., O. Ode, M. Ndoye, and B. Mair. 2019. ”MAP Image Reconstruction for Landmine and IED Detection Using Ground Penetrating Radar.” International Journal of Remote Sensing 40 (22): 8298–8321. Taylor\& Francis. doi:10.1080/01431161.2019.1608385.
   Web of Science ®Google Scholar
• Ayala-Cabrera, D., M. Herrera, J. Izquierdo, and R. Pérez-García. 2014. “GPR Data Analysis Using Multi-Agent and Clustering Approaches: A Tool for Technical Management of Water Supply Systems.” Digital Signal Processing 27: 140–149. doi:10.1016/j.dsp.2013.12.012.
   Web of Science ®Google Scholar
• Baili, J., S. Lahouar, M. Hergli, A. Amimi, and K. Besbes. 2005. “Denoising of GPR Signals Based on the Discrete Wavelet Transform.” In Proceedings of the Second International Conference on Machine Intelligence Tozeur, Tunisia, 103–134.
   Google Scholar
• Baili, J., S. Lahouar, M. Hergli, I. L. Al-Qadi, and K. Besbes. 2009. ”GPR Signal De-Noising by Discrete Wavelet Transform.” Ndt & E International 42 (8): 696–703. Elsevier. doi:10.1016/j.ndteint.2009.06.003.
   Web of Science ®Google Scholar
• Bao, Q. Z., Q. C. Li, and W. Chao Chen. 2014. “GPR Data Noise Attenuation on the Curvelet Transform.” Applied Geophysics 11 (3): 301–310. doi:10.1007/s11770-014-0444-2.
   Web of Science ®Google Scholar
• Batson, J. and L. Royer. 2019. ”Noise2Self: Blind Denoising by Self-Supervision International Conference on Machine Learning Long Beach, California.” 524–533.
   Google Scholar
• Buchholz, T. O., A. Krull , and F. Jug. 2019. “Noise2void - Learning Denoising from Single Noisy Images Supplementary Note.” Proceedings - International Symposium on Biomedical Imaging Dresden, Germany : 2129–2137.
   Google Scholar
• Candès, E. J. and D. L. Donoho. 2004. ”New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2 Singularities.” Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences 57 (2): 219–266. Wiley Online Library. doi:10.1002/cpa.10116.
   Google Scholar
• Canny, J. 1986. ”A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence (6): 679–698. IEEE. doi:10.1109/TPAMI.1986.4767851.
   PubMed Web of Science ®Google Scholar
• Cui, F., M. Ning, J. Shen, and X. Shu. 2022. ”Automatic Recognition and Tracking of Highway Layer-Interface Using Faster R-CNN.” Journal of Applied Geophysics 196: 104477. Elsevier. doi:10.1016/j.jappgeo.2021.104477.
   Web of Science ®Google Scholar
• Di Giacomo, G., L. De Giorgi, I. Ditaranto, G. Leucci, I. Miccoli, and G. Scardozzi. 2018. ”The Medieval Cave Village of Casalrotto (Mottola, Apulia): New Data on the Settlement and Its Necropolis from Archaeological and Geophysical Measurements.” Measurement 128: 96–103. Elsevier. doi:10.1016/j.measurement.2018.06.038.
   Web of Science ®Google Scholar
• Donoho, D. L. and J. M. Johnstone. 1994. ”Ideal Spatial Adaptation by Wavelet Shrinkage.” Biometrika 81 (3): 425–455. Oxford University Press. doi:10.1093/biomet/81.3.425.
   Web of Science ®Google Scholar
• Easley, G., D. Labate, and W.-Q. Lim. 2008. ”Sparse Directional Image Representations Using the Discrete Shearlet Transform.” Applied and Computational Harmonic Analysis 25 (1): 25–46. Elsevier. doi:10.1016/j.acha.2007.09.003.
   Web of Science ®Google Scholar
• Giusti, E., D. Cataldo, A. Bacci, S. Tomei, and M. Martorella. 2018. ”ISAR Image Resolution Enhancement: Compressive Sensing versus State-Of-The-Art Super-Resolution Techniques.” IEEE Transactions on Aerospace and Electronic Systems 54 (4): 1983–1997. IEEE. doi:10.1109/TAES.2018.2807283.
   Web of Science ®Google Scholar
• Guo, K., G. Kutyniok, and D. Labate. 2006. Sparse Multidimensional Representations Using Anisotropic Dilation and Shear Operators.” Citeseer.
   Google Scholar
• IDS. 2008. User’s Manual of RIS K2-Fastwave a Field Acquisition Unit: Operative Instructions to the Use of RIS K2-Fastwave a Field Georadar System (Version: 3.1). Pisa, Italy.
   Google Scholar
• Jazayeri, S., N. Kazemi, and S. Kruse. 2019. ”Sparse Blind Deconvolution of Ground Penetrating Radar Data.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 3703–3712. IEEE. doi:10.1109/TGRS.2018.2886741.
   Web of Science ®Google Scholar
• Jol, H. M. 2008. Ground Penetrating Radar Theory and Applications. Amsterdam, The Netherlands: Elsevier.
   Google Scholar
• Kaiming, H., J. Sun, and X. Tang. 2012. ”Guided Image Filtering.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (6): 1397–1409. IEEE. doi:10.1109/TPAMI.2012.213.
   Web of Science ®Google Scholar
• Kumlu, D. and I. Erer. 2018. ”The Multiscale Directional Neighborhood Filter and Its Application to Clutter Removal in GPR Data.” Signal, Image and Video Processing 12 (7): 1237–1244. Springer London. doi:10.1007/s11760-018-1275-z.
   Web of Science ®Google Scholar
• Kumlu, D., I. Erer, and N. Huseyin Kaplan. 2020. “Low Complexity Clutter Removal in GPR Images via Lattice Filters.” Digital Signal Processing 101: 102724. doi:10.1016/j.dsp.2020.102724.
   Web of Science ®Google Scholar
• Lei, W., F. Hou, J. Xi, Q. Tan, M. Xu, X. Jiang, G. Liu, and Q. Gu. 2019. ”Automatic Hyperbola Detection and Fitting in GPR B-Scan Image.” Automation in Construction 106 (January): 102839. Elsevier. doi:10.1016/j.autcon.2019.102839.
   Web of Science ®Google Scholar
• Li, J. H., H. Zhan Xiang, J. Yang, S. Jun Meng, L. Wen Jie, and X. Qian Liao. 2019. “Scale and Rotation Statistic-Based Self-Adaptive Function for Ground Penetrating Radar Denoising in Curvelet Domain.” Wuli Xuebao/acta Physica Sinica 68 (9). doi:10.7498/aps.68.20182061.
   Google Scholar
• Liu, C., C. Song, and Q. Lu. 2017. “Random Noise De-Noising and Direct Wave Eliminating Based on SVD Method for Ground Penetrating Radar Signals.” Journal of Applied Geophysics 144: 125–133. doi:10.1016/j.jappgeo.2017.07.007.
   Web of Science ®Google Scholar
• Ostoori, R., A. Goudarzi, and B. Oskooi. 2018. ”GPR Random Noise Reduction Using BPD and EMD.” Journal of Geophysics and Engineering 15 (2): 2–353347–353. IOP Publishing. doi:10.1088/1742-2140/aa8cb4.
   Web of Science ®Google Scholar
• Pan, J., M. Sun, Y. Wang, C. Le Bastard, and V. Baltazart. 2020. ”A Time-Delay Estimation Approach for Coherent GPR Signals by Taking into Account the Noise Pattern and Radar Pulse.” Signal Processing 176: 107654. Elsevier. doi:10.1016/j.sigpro.2020.107654.
   Web of Science ®Google Scholar
• Pham, M. T. and S. Lefèvre. 2018. “Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN.” International Geoscience and Remote Sensing Symposium (IGARSS) 2018-July: 6804–6807. doi:10.1109/IGARSS.2018.8517683.
   Google Scholar
• Quan, Y., M. Chen, T. Pang, and H. Ji. 2020. “Self2self with Dropout: Learning Self-Supervised Denoising from Single Image.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1887–1895. doi:10.1109/CVPR42600.2020.00196.
   Google Scholar
• Ren, S., K. He, R. Girshick, and J. Sun. 2015. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” Advances in Neural Information Processing Systems 28 91–99.
   Google Scholar
• Shong, W. and H. Wu. 2004. “The Application of the Wavelet Transform Technique to Data Processing in GPR.” Geophysical and Geochemical Exploration 1 69–72.
   Google Scholar
• Tbarki, K., S. Ben Said, R. Ksantini, and Z. Lachiri. 2018. ”Covariance-Guided Landmine Detection and Discrimination Using Ground-Penetrating Radar Data.” International Journal of Remote Sensing 39 (2): 289–314. Taylor\& Francis. doi:10.1080/01431161.2017.1382746.
   Web of Science ®Google Scholar
• Utsi, E. C. 2017. Ground Penetrating Radar: Theory and Practice. Oxford, UK: Butterworth-Heinemann.
   Google Scholar
• Van Kempen, L. M., H. Sahli, J. Brooks, and J. P. Cornelis. 2000. “New Results on Clutter Reduction and Parameter Estimation for Land Mine Detection Using GPR Eighth International Conference on Ground Penetrating Radar Gold Coast, Australia.” 4084: 872–879.
   Google Scholar
• Wang, X. and S. Liu. 2016. ”Noise Suppressing and Direct Wave Arrivals Removal in GPR Data Based on Shearlet Transform.” Signal Processing 132: 227–242. Elsevier. doi:10.1016/j.sigpro.2016.05.007.
   Web of Science ®Google Scholar
• Warren, C., A. Giannopoulos, and I. Giannakis. 2016. ”GprMax: Open Source Software to Simulate Electromagnetic Wave Propagation for Ground Penetrating Radar.” Computer Physics Communications 209 : 163-170. Elsevier.
   Web of Science ®Google Scholar
• Wen, J., Z. Li, and J. Xiao. 2020. “Noise Removal in Tree Radar B-Scan Images Based on Shearlet.” Wood Research 65 (1): 1–12. doi:10.37763/wr.1336-4561/65.1.001012.
   Web of Science ®Google Scholar
• Wu, X., Y. Gao, and L. You. 2021. ”Non-Destructive Location Technology of Concealed Power Facilities Based on a Two-Step Detection Method.” International Journal of Remote Sensing 42 (17): 6603–6623. Taylor\& Francis. doi:10.1080/01431161.2021.1939915.
   Web of Science ®Google Scholar
• Zhang, K., W. Zuo, Y. Chen, D. Meng, and L. Zhang. 2017. ”Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.” IEEE Transactions on Image Processing 26 (7): 3142–3155. IEEE. doi:10.1109/TIP.2017.2662206.
   PubMed Web of Science ®Google Scholar
• Zou, H., Y. Sui, J. Xu, and S. Ning. 2005. “Study on Methods of GPR Image De-Noising Based on Multi-Wavelets Transform.” Acta Simulata Systematica Sinica 17 (4): 855–862.
   Google Scholar