Reduction of Multiplicative Noise in Radar Images

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.
Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.
Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.
Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).
Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

1. Tuzova A. A., Pavlov V. A., Belov A. A. Primenenie plat-formy Jetson TX1 dlja realizacii algoritmov formirovanija radiolokacionnyh izobrazhenij radiolokatora s sintezirovannoj aperturoj. Nedelja nauki SPBPU 2019: materialy nauchnoj konferencii s mezhdunarodnym uchastiem. SPb, Izd-vo Politehn. un-ta, 2019, pp. 26-29. (In Russ.)

2. Pavlov V. A., Belov A. A., Tuzova A. A. Implementation of Synthetic Aperture Radar Processing Algorithms on the Jetson TX1 Platform. IEEE Intern. Conf. on Electrical Engineering and Photonics (EExPolytech). St Petersburg, 2019, pp. 90-93. doi: 10.1109/EExPolytech.2019.8906850

3. Volkov V. Ju. Adaptivnoe vydelenie melkih ob’ektov na cifrovyh izobrazhenijah. Radioelectronics. 2017, no. 1, pp. 17-28. (In Russ.)

4. Fursov V., Zherdev D., Kazanskiy N. Support subspaces method for synthetic aperture radar automatic target recognition. Intern. J. of Advanced Robotic Systems. 2016, vol. 13, iss. 5, pp. 1-11. doi: 10.1177/1729881416664848

5. Domg Y., Milne A. K., Forster B. C. Toward edge sharpening: a SAR speckle filtering algorithm. IEEE Transactions on Geoscience and Remote Sensing. Apr. 2001, vol. 39, no. 4, pp. 851-863. doi: 10.1109/36.917910

6. Yongjian Yu., Acton S. T. Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing. Nov. 2002, vol. 11, no. 11, pp. 1260-1270. doi: 10.1109/TIP.2002.804276

7. Gomez L., Buemi M. E., Jacobo-Berlles J. C., Mejail M. E. A New Image Quality Index for Objectively Evaluating Despeckling Filtering in SAR Images. IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing. 2016, vol. 9, no. 3, pp. 1297-1307. doi: 10.1109/JSTARS.2015.2465167

8. Huang X., Jia Z., Zhou J., Yang J., Kasabov N. Speckle Reduction of Reconstructions of Digital Holograms Using Gamma-Correction and Filtering. IEEE Access. 2018, vol. 6, pp. 5227-5235. doi: 10.1109/ACCESS.2017.2751540

9. Starovoitov V. V. A method for selecting a filter for smoothing the speckle noise of radar images with a synthesized aperture. Computer science. 2015, no. 2, pp. 5-11. (In Russ.)

10. Bobkova A., Porshnev S., Zjuzin V., Bobkov V. Issledovanie metodov udalenija spekl-shumov na ul'trazvukovyh izobrazhenijah. The 23rd Intern. Conf. on Computer Graphics and Vision, Vladivostok, Sept. 2013, pp. 244-246. (In Russ.)

11. Touzi R. A review of speckle filtering in the context of estimation theory. IEEE Transactions on Geoscience and Remote Sensing. 2002, vol. 40, iss. 11, pp. 2392-2404. doi: 10.1109/TGRS.2002.803727

12. Aja-Fernandez S., Alberola-Lopez C. On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Transactions on Image Processing. Sept. 2006, vol. 15, no. 9, pp. 2694-2701. doi: 10.1109/TIP.2006.877360

13. Krissian K., Westin C., Kikinis R., Vosburgh K. G. Oriented Speckle Reducing Anisotropic Diffusion. IEEE Transactions on Image Processing. May 2007, vol. 16, no. 5, pp. 1412-1424. doi: 10.1109/TIP.2007.891803

14. Jong-Sen Lee, Jen-Hung Wen, Ainsworth T. L., KunShan Chen, Chen A. J. Improved Sigma Filter for Speckle Filtering of SAR Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2009, vol. 47, no. 1, pp. 202-213. doi: 10.1109/TGRS.2008.2002881

15. Karabchevsky S., Kahana D., Ben-Harush O., Guterman H. FPGA-Based Adaptive Speckle Suppression Filter for Underwater Imaging Sonar. IEEE J. of Oceanic Engineering. Oct. 2011, vol. 36, no. 4, pp. 646-657. doi: 10.1109/JOE.2011.2157729

16. Dong X., Zhang D., Cui K., Hu C., Lv X. Spatial filtering strategies on deforestation detection using SAR image textures. 2016 CIE Intern. Conf. on Radar (RADAR), Guangzhou, China, 2016, pp. 1-4. doi: 10.1109/RADAR.2016.8059472

17. Ramos-Llordén G., Vegas-Sánchez-Ferrero G., Martin-Fernandez M., Alberola-López C., Aja-Fernández S. Anisotropic Diffusion Filter With Memory Based on Speckle Statistics for Ultrasound Images. IEEE Transactions on Image Processing. Jan. 2015, vol. 24, no. 1, pp. 345-358, doi: 10.1109/TIP.2014.2371244

18. Paul A., Mukherjee D. P., Acton S. T. Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images. IEEE J Biomed Health Inform. Jan. 2019, vol. 23, iss. 1, pp. 264-272. doi: 10.1109/JBHI.2018.2791624

19. Gonzalez R. C., Woods R. E. Digital Image Processing. 2nd. ed. Addison-Wesley Longman Publishing Co., Inc., USA, New Jersey 07458. 2001, 191 p.

20. Lee Jong-Sen Digital Image Enhancement and Noise Filtering by Use of Local Statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence. March 1980, vol. PAMI-2, no. 2, pp. 165-168. doi: 10.1109/TPAMI.1980.4766994

21. Frost V. S., Stiles J. A., Shanmugan K. S., Holtzman J. C. A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence. March 1982, vol. PAMI-4, no. 2, pp. 157-166. doi: 10.1109/TPAMI.1982.4767223

22. Kuan D., Sawchuk A., Strand T., Chavel P. Adaptive restoration of images with speckle. IEEE Transactions on Acoustics, Speech and Signal Processing. March 1987, vol. 35, no. 3, pp. 373-383. doi: 10.1109/TASSP.1987.1165131

23. Tomasi C., Manduchi R. Bilateral filtering for gray and color images. Sixth Intern. Conf. on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India, 1998, pp. 839-846. doi: 10.1109/ICCV.1998.710815

24. Lopes A., Nezry E., Touzi R., Laur H. Structure detection and statistical adaptive speckle filtering in SAR images. Intern. J. of Remote Sensing. 1993, vol. 14, iss. 9, pp. 1735-1758, doi: 10.1080/01431169308953999

25. Perona P., Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990, vol. 12, iss. 7, pp. 629-639. doi: 10.1109/34.56205

26. Wang Z., Bovik A. C., Sheikh H. R., Simoncelli E. P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 2004, vol. 13, iss. 4, pp. 600–612. doi: 10.1109/TIP.2003.819861

27. Wang Z., Bovik A. C. A universal image quality index. IEEE Signal Processing Lett. 2002, vol. 9, iss. 3, pp. 81–84. doi: 10.1109/97.995823

28. Xue W., Zhang L., Mou X., Bovik A. C. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Transactions on Image Processing. 2014, vol. 23, iss. 2, pp. 684–695. doi: 10.1109/TIP.2013.2293423

29. Tuzova A. A., Pavlov V. A., Belov A. A., Volvenko S. V. Comparison of Image Quality Assessment Metrics for Evaluation of Performance of Anisotropic Diffusion Filter for SAR Images. 2020 IEEE Intern. Conf. on Electrical Engineering and Photonics (EExPolytech), St Petersburg, 2020, pp. 176-179. doi: 10.1109/EExPolytech50912.2020.9243957

30. Abramov S., Abramova V., Lukin V., Ponomarenko N., Vozel B., Chehdi K., Egiazarian K., Astol Ja. Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data. Computational and Numerical Simulations. 2014, ch. 24, pp. 303-327. doi: 10.5772/57040

31. Choi H., Jeong J. Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sensing. May 2019, vol. 11, iss. 1184, pp. 1-27. doi: 10.3390/rs11101184

32. Xie Hua, Pierce L. E., Ulaby F. T. Statistical properties of logarithmically transformed speckle. IEEE Transactions on Geoscience and Remote Sensing. Mar. 2002, vol. 40, iss. 3, pp. 721-727. doi: 10.1109/TGRS.2002.1000333

33. Singh P., Pandey R. Speckle noise: Modelling and implementation. International Journal of Circuit Theory and Applications. 2016, vol. 9, iss. 17, pp. 8717–8727.

34. Herman C., Lehmann E. L. The use of maximum likelihood estimates in χ2 tests for goodness of fit. Ann. Math. Statist. Sep. 1954, vol. 25, iss. 3, pp. 579-586. doi: 10.1214/aoms/1177728726

35. Pearson K. On the criterion that a given system of deviations from theprobable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Breakthroughs in Statistics: Methodology and Distribution. Eds. S. Kotz, N. L. Johnson. New York. Springer New York, 1992, pp. 11–28. doi: 10.1007/978-1-4612-4380-9_2

36. Belov A. A., Pavlov V. A., Tuzova A. A. A Method of Finding Optimal Parameters of Speckle Noise Reduction Filters. 2020 Internet of Things, Smart Spaces and Next Generation Networks and Systems, Springer Intern. Publishing, 2020, pp. 133-141. doi: 10.1007/978-3-030-65729-1_12

37. Tuzova A. A. The project to find the optimal parameters of speckle noise reduction filters. FilteringSpeckleNoise_main_script.m file. Available at: https://github.com/AnnaTuzova/Speckle-noise-project (accessed 25.04.2021)