A robust framework for removing G⁰ distributed noise from Synthetic Aperture Radar images

References

• Amirmazlaghani, M., & Amindavar, H. (2009). A novel statistical approach for speckle filtering of SAR images. In 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop. IEEE. https://doi.org/10.1109/DSP.2009.4785967
   Google Scholar
• Anandhi, D., & Valli, S. (2018). An enhanced approach to Despeckle SAR images. Radioengineering, 27(3), 864–875. https://doi.org/10.13164/re.2018.0864
   Web of Science ®Google Scholar
• Bhattacharya, A. (n.d.). Speckle filtering or speckle statistics, Retrieved from http://www.csre.iitb.ac.in/avikb/GNR647/Lec11Speckle.pdf.
   Google Scholar
• Buades, Antoni, Bartomeu, Coll, & Morel, J.-M. (2005). A non-local algorithm for image denoising Computer Vision and Pattern Recognition. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol. 2, pp. 60–65.
   Google Scholar
• Chen, W. F., Liu, A. L., Xia, J. J., Duan, C. L., Yang, S. S., & Hu, W. C. (2014). Study on synthetic aperture radar image denoising algorithm. Applied Mechanics and Materials, 599–601, 1734–1737. https://doi.org/10.4028/www.scientific.net/AMM.599-601.1734
   Google Scholar
• Chitroub, S., Houacine, A., & Sansal, B. (2002). Statistical characterisation and modeling of SAR images. Signal Processing, 82(1), 69–92. https://doi.org/10.1016/S0165-1684(01)00158-X
   Web of Science ®Google Scholar
• Choi, H., & Jeong, J. (2019). Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sensing, 11(10), 1184. https://doi.org/10.3390/rs11101184
   Web of Science ®Google Scholar
• Choi, H., Yu, S., & Jeong, J. (2019). Speckle noise removal technique in SAR images using SRAD and weighted least squares filter. In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). IEEE. https://doi.org/10.1109/ISIE.2019.8781457
   Google Scholar
• Delignon, Y., Garello, R., & Hillion, A. (1997). Statistical modeling of ocean SAR images. IEE Proceedings-Radar, Sonar and Navigation, 144(6), 348–354.
   Google Scholar
• Du, Y., Zhong, R., Li, Q., & Zhang, F. (2022). TransUNet++SAR: Change detection with deep learning about architectural ensemble in SAR images. Remote Sensing, 15(1), 6. https://doi.org/10.3390/rs15010006
   Web of Science ®Google Scholar
• Frery, A. C., Correa, A., Renno, C. D., Corina da da, C. F., Jacobo-Berlles, J., Vasconcellos, K. L., Mejail, M., & Sant'anna, S. J. (1999a). Models for synthetic aperture radar image analysis. Resenhas Do Instituto de Matemática e Estatística da Universidade de São Paulo, 4(1), 45–77.
   Google Scholar
• Frery, A. C., Freitas, C., Sant'anna, S. J., & Rennó, C. D. (1999b). Statistical properties of SAR data and their consequences. United Nations Programme on Space Applications, 10, 53.
   Google Scholar
• Frost, V. S., Stiles, J. A., Shanmugan, K. S., & Holtzman, J. C. (1982). A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(2), 157–166. https://doi.org/10.1109/tpami.1982.4767223
   PubMed Web of Science ®Google Scholar
• Gao, G. (2010). Statistical modeling of SAR images: A survey. Sensors, 10(1), 775–795. https://doi.org/10.3390/s100100775
   PubMed Web of Science ®Google Scholar
• Gayathri, R., & Sabeenian, R. S. (2012). A Survey on Image Denoising Algorithms (IDA). International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 1(5), 456–462.
   Google Scholar
• Goyal, B., Gupta, A., Dogra, A., & Koundal, D. (2022). An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising. International Journal of Cognitive Computing in Engineering, 3, 90–97. https://doi.org/10.1016/j.ijcce.2022.03.001
   Google Scholar
• Halidou, A., Mohamadou, Y., Ari, A. A. A., & Zacko, E. J. G. (2023). Review of wavelet denoising algorithms. Multimedia Tools and Applications, 82(27), 41539–41569. https://doi.org/10.1007/s11042-023-15127-0
   Web of Science ®Google Scholar
• Hao, Y., & Xu, J. (2014). An effective dual method for multiplicative noise removal. Journal of Visual Communication and Image Representation, 25(2), 306–312. https://doi.org/10.1016/j.jvcir.2013.11.004
   Web of Science ®Google Scholar
• He, K., Sun, J., & Tang, X. (2012). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409. https://doi.org/10.1109/TPAMI.2012.213
   Web of Science ®Google Scholar
• Henri, M. (2008). Processing of synthetic aperture radar images. Willey.
   Google Scholar
• Huang, Y.-M., Ng, M. K., & Wen, Y.-W. (2009). A new total variation method for multiplicative noise removal. SIAM Journal on Imaging Sciences, 2(1), 20–40. https://doi.org/10.1137/080712593
   Web of Science ®Google Scholar
• Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44(13), 800–801. https://doi.org/10.1049/el:20080522
   Web of Science ®Google Scholar
• Jebur, R., Sabah., & C. S., Der. (2023). Dalal Adulmohsin Hammood, and Leong Yeng Weng. Image denoising techniques: An overview. In AIP Conference Proceedings, vol. 2804, no. 1. AIP Publishing.
   Google Scholar
• Kumar, B., Kumar Ranjan, R., & Husain, A. (2021). A multi-objective enhanced fruit fly optimization (MO-EFOA) framework for despeckling SAR images using DTCWT based local adaptive thresholding. International Journal of Remote Sensing, 42(14), 5493–5514. https://doi.org/10.1080/01431161.2021.1921875
   Web of Science ®Google Scholar
• Lattari, F., Gonzalez Leon, B., Asaro, F., Rucci, A., Prati, C., & Matteucci, M. (2019). Deep learning for SAR image despeckling. Remote Sensing, 11(13), 1532. https://doi.org/10.3390/rs11131532
   Web of Science ®Google Scholar
• Le Toan, T. (n.d.). Advanced training course on land remote sensing, Retrieved from https://earth.esa.int/landtraining07/D1LA1-LeToan.pdf.
   Google Scholar
• Lee, J.-S. (1981). Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing, 17(1), 24–32. https://doi.org/10.1016/S0146-664X(81)80005-6
   Google Scholar
• Liang, D., Jiang, M., & Ding, J. (2022). Fast patchwise nonlocal SAR image despeckling using joint intensity and structure measures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6283–6293. https://doi.org/10.1109/JSTARS.2022.3195093
   Web of Science ®Google Scholar
• Liu, S., Pu, N., Cao, J., & Zhang, K. (2022). Synthetic Aperture radar image despeckling based on multi-weighted sparse coding. Entropy, 24(1), 96. https://doi.org/10.3390/e24010096
   PubMed Web of Science ®Google Scholar
• Liu, X., Tanaka, M., & Okutomi, M. (2013). Single-image noise level estimation for blind denoising. IEEE Transactions on Image Processing, 22(12), 5226–5237. https://doi.org/10.1109/TIP.2013.2283400
   PubMed Web of Science ®Google Scholar
• Lopez-Martinez, C. (2013). Speckle noise characterization and filtering in polarimetric SAR data, Eur. Space Agency, Paris, France. Retrieved fom https://earth.esa.int/documents/10174/669756/Speckle_Noise_Characterisation.pdf.
   Google Scholar
• Ma, X., Hu, H., & Wu, P. (2022). A no-reference edge-preservation assessment index for SAR image filters under a bayesian framework based on the ratio gradient. Remote Sensing, 14(4), 856. https://doi.org/10.3390/rs14040856
   Web of Science ®Google Scholar
• Majee, S., Ray, R. K., & Majee, A. K. (2022). A new non-linear hyperbolic-parabolic coupled PDE model for image despeckling. In IEEE Transactions on Image Processing, 31, 1963–1977. https://doi.org/10.1109/TIP.2022.3149230
   PubMed Web of Science ®Google Scholar
• Martinez-Espla, J. J., Martinez-Marin, T., & Lopez-Sanchez, J. M. (2009). A particle filter approach for InSAR phase filtering and unwrapping. IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1197–1211. https://doi.org/10.1109/TGRS.2008.2008095
   Web of Science ®Google Scholar
• Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1), 6–43. https://doi.org/10.1109/MGRS.2013.2248301
   Web of Science ®Google Scholar
• Mu, Y., Huang, B., Pan, Z., Yang, H., Hou, G., & Duan, J. (2019). An enhanced high-order variational model based on speckle noise removal with G0 distribution. IEEE Access, 7, 104365–104379. https://doi.org/10.1109/ACCESS.2019.2931581
   Web of Science ®Google Scholar
• Mullissa, A. G., Marcos, D., Tuia, D., Herold, M., & Reiche, J. (2022). deSpeckNet: Generalizing deep learning-based SAR image despeckling. In IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15. https://doi.org/10.1109/TGRS.2020.3042694
   Web of Science ®Google Scholar
• NASA PHOTOJOURNAL – jet population Laboratories. (n.d.). Retrieved from https://photojournal.jpl.nasa.gov/.
   Google Scholar
• Oliver, C., & Quegan, S. (2004). Understanding synthetic aperture radar images. SciTech Publishing.
   Google Scholar
• Painam, R. K., & Suchetha, M. (2023). Despeckling of SAR images using BEMD-based adaptive frost filter. Journal of the Indian Society of Remote Sensing, 51(9), 1879–1890. https://doi.org/10.1007/s12524-022-01495-x
   Web of Science ®Google Scholar
• Parhad, S. V., Warhade, K. K., & Shitole, S. S. (2023). Speckle noise reduction in sar images using improved filtering and supervised classification. Multimedia Tools and Applications, 83(18), 54615–54636. https://doi.org/10.1007/s11042-023-17648-0
   Google Scholar
• Podest, E. (2017). Basics of synthetic aperture radar.
   Google Scholar
• Rafati, M., Kalantari, N., Azadbakht, J., Nickfarjam, A. M., & Hosseini, F. (2023). Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region. Multimedia Tools and Applications, 2023, 1–49. https://doi.org/10.1007/s11042-023-15341-w
   Google Scholar
• Rai, R. K., Asnani, J., & Sontakke, T. R. (2012). Review of shrinkage techniques for image denoising. International Journal of Computer Applications, 42(19), 13–16. https://doi.org/10.5120/5799-8009
   Google Scholar
• Raju, K. M. S., Nasir, M. S., & Meera Devi, T. (2013). Filtering techniques to reduce speckle noise and image quality enhancement methods on satellite images. IOSR Journal of Computer Engineering, 15(4), 10–15. https://doi.org/10.9790/0661-1541015
   Google Scholar
• Rathore, M. S. (2014). Statistical analysis of Synthetic Aperture Radar (SAR) image speckle Diss.
   Google Scholar
• Ray, A., & Kartikeyan, B. (2015). Denoising techniques for synthetic aperture radar data – A review. International Journal of Computer Engineering and Technology, 6(9), 1–11.
   Google Scholar
• Roy, N., & Jain, V. (2015). Additive and multiplicative noise removal by using gradient histogram preservations approach. International Journal of Computer Applications, 130(2), 11–16. https://doi.org/10.5120/ijca2015906876
   Google Scholar
• Sandia National Laboratories of U.S Government. (n.d.). Retrieved from https://www.sandia.gov/.
   Google Scholar
• Satellite Imagery – Airbus Defense and Space. (n.d.). Retrieved from https://www.airbus.com/space/earth-observation.html.
   Google Scholar
• Sattar, F., Floreby, L., Salomonsson, G., & Lovstrom, B. (1997). Image enhancement based on a nonlinear multiscale method. IEEE Transactions on Image Processing, 6(6), 888–895. https://doi.org/10.1109/83.585239
   PubMed Web of Science ®Google Scholar
• Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20–34. https://doi.org/10.1016/j.engappai.2019.01.001
   Web of Science ®Google Scholar
• Singh, P., & Shree, R. (2016). Statistical modeling of log transformed speckled image. International Journal of Computer Science and Information Security, 14(8), 426.
   Google Scholar
• Singh, P., Diwakar, M., Shankar, A., Shree, R., & Kumar, M. (2021). A review on SAR Image and its despeckling. Archives of Computational Methods in Engineering, 28(7), 4633–4653. https://doi.org/10.1007/s11831-021-09548-z
   Web of Science ®Google Scholar
• Singh, P., Shankar, A., & Diwakar, M. (2022). Review on nontraditional perspectives of synthetic aperture radar image despeckling. Journal of Electronic Imaging, 32(02), 021609–021609. https://doi.org/10.1117/1.JEI.32.2.021609
   Google Scholar
• Tao, S., Li, X., Ye, X., Wang, H., & Li, X. (2023). SAR image despeckling using a CNN guided by high-frequency information. Journal of Electromagnetic Waves and Applications, 37(3), 441–451. https://doi.org/10.1080/09205071.2022.2145506
   Web of Science ®Google Scholar
• The Copernicus Open Access Hub (previously known as Sentinels Scientific Data Hub). (n.d.). Retrieved from https://scihub.copernicus.eu/.
   Google Scholar
• Vitale, S., Ferraioli, G., & Pascazio, V. (2022). Analysis on the building of training dataset for deep learning SAR despeckling. In IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2021.3091287
   Web of Science ®Google Scholar
• Wang, C., Guo, B., & He, F. (2023). A Novel SAR Image Despeckling Method Based on Local Filter With Nonlocal Preprocessing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2915–2930. https://doi.org/10.1109/JSTARS.2023.3258424
   Web of Science ®Google Scholar
• Wang, C., Yin, Z., Ma, X., & Yang, Z. (2022). SAR image despeckling based on block-matching and noise-referenced deep learning method. Remote Sensing, 14(4), 931. https://doi.org/10.3390/rs14040931
   Web of Science ®Google Scholar
• Wang, K., Li, Z., & Zhang, Y. (2022). Reduction in ultrasound images of the common carotid artery based on integer and fractional-order total variation. Ultrasonic Imaging, 44(4), 123–141. https://doi.org/10.1177/01617346221096840
   PubMed Web of Science ®Google Scholar
• Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar
• Wen, Z., He, Y., Yao, S., Yang, W., & Zhang, L. (2023). A self-attention multi-scale convolutional neural network method for SAR image despeckling. International Journal of Remote Sensing, 44(3), 902–923. https://doi.org/10.1080/01431161.2023.2173029
   Web of Science ®Google Scholar
• Xie, H., Pierce, L. E., & Ulaby, F. T. (2002). Statistical properties of logarithmically transformed speckle, IEEE Transactions on Geoscience and Remote Sensing 40(3): 721–727. https://doi.org/10.1109/TGRS.2002.1000333
   Web of Science ®Google Scholar
• Yommy, A. S., Liu, R., Onuh, S. O., & Ikechukwu, A. C. (2015). SAR image despeckling and compression using K-nearest neighbour based lee filter and wavelet. In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE. https://doi.org/10.1109/CISP.2015.7407868
   Google Scholar
• Zhou, Z., Lam, E. Y., & Lee, C. (2019). Nonlocal means filtering based speckle removal utilizing the maximum a posteriori estimation and the total variation image prior. IEEE Access, 7, 99231–99243. https://doi.org/10.1109/ACCESS.2019.2929364
   Web of Science ®Google Scholar