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A New Objective Evaluation Index for Despeckled SAR Images | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 5، دوره 50، شماره 2، اسفند 2018، صفحه 141-146 اصل مقاله (2.05 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2018.14133.5091 | ||
| نویسندگان | ||
| H. Markarian1؛ M. Nejati-Jahromi* 2 | ||
| 1Department of Electrical Engineering, Islamic Azad University South Tehran Branch, Tehran, Iran | ||
| 2Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology Tehran, Iran | ||
| چکیده | ||
| Synthetic aperture radar (SAR) images due to the usage of coherent imaging systems are affected by speckle. Thus, lots of despeckling filters have been introduced up to now to suppress the speckle. Hence, objective and subjective evaluations of the denoised SAR images become necessity. Many objective evaluating estimators have been introduced to evaluate the performance of despeckling filters. However, two main problems exist when evaluating the SAR images: 1) contradiction of objective and subjective evaluations and 2) absence of the ground-truth (noiseless) SAR image of the illuminated scene. Lots of efforts had been made to introduce precise referenceless estimators for SAR images which will be compatible with subjective evaluation and the results obtained by other estimators. In this paper, we propose a new edge detector and also a new referenceless estimator called “Extended Ratio Edge Detector” and “E-αβ”, respectively. These algorithms are the extended version of “Ratio Edge Detector” and “αβ” estimator. Experiments on images obtained from RADARSAT-1 dataset showed that the proposed edge detector and the estimator outperform their previous versions of algorithms as the proposed E-αβ parameter subjectively reports up to 0.2 better results for images filtered with FANS filter in comparison with other used methods. This is also validated by βratio and μratio parameters. Therefore, it is a reliable tool for objective evaluation of despeckled SAR images. | ||
| کلیدواژه‌ها | ||
| Synthetic Aperture Radar (SAR)؛ Despeckling؛ Edge Detection؛ Objective Evaluation | ||
| مراجع | ||
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[1] Preetha, C. Gorthi, S. and S. Mishra, D. Compressive Sensing Framework for Simultaneous Compression and Despeckling of SAR images, IEEE Eighth International Conference on Advances in Pattern Recognition (ICAPR), 15 (2015) 1-6. [2] Xiao Feng Li, Ju Xu, Ji Jun Lou, Lijia Cao, Shengxiu Zhang, Intensity image denoising for laser active imaging system using non-subsampled contourlet transform and SURE approach, Elsevier, Optik International Journal for Light and Electron Optics, 123(9) (2012) 808-813. [3] O.V. Michailovich, A. Tannenbaum, Despeckling of medical ultrasound images IEEE Trans. On Ultrasonics, Ferroelectrics and Frequency Control, 53(1), (2006) 64-78. [4] F. Chaillon, C. Fraschini, P. Courmontagne, Speckle noise reduction in SAS imagery Elsevier Journal of Signal Processing , 87(4) (2007) 762-781. [5] F. Argenti, A. Lapini, T. Bianchi, A tutorial on speckle reduction in synthetic aperture radar images, IEEE Geoscience and Remote Sensing Magazine, 1(3) (2013) 6-35. [6] J. Goodman, Some fundamental properties of speckle Journal of the Optical Society of America, 66 (1976) 1145-1150. [7] T. R. Crimmins, Geometric filter for reducing speckle Optical Engineering, 25(5) (1986) 651-654. [8] Y. Wu and H. Maitre, Smoothing speckled synthetic aperture radar images by using maximum homogenoues region filters, Optical Engineering, 31(8) (1992) 1785-1792. [9] F. Tupin, M. Sigelle, A. Chkeif and J-P Veran, Restoration of SAR images using recovery of discontinuities and non-linear optimization, In J. Van Leuween, G. Goos, J. Hartemis editor, EMMCVPR’97, Lecture notes in Computer Science, Springer, (1997). [10] M. Koosha, K. Hajsadeghi and M. Koosha, Fine logarithmic adaptive soft morphological algorithm for synthetic aperture radar image segmentation, IET Journal of Image Processing, 8(2) (2014) 90-102. [11] J. Bruniquel and A. Lopez, Analysis and enhancement of multi-temporal SAR data, Proc. SPIE 2315, Image and signal Processing for Remote Sensing, 342 (1994) doi:101117/12.196733. [12] L. Rudin, P. L. Lions and S. Osher, Multiplicative denoising and deblurring: theory and algorithm, Geometric Level Sets in Imaging, Vision and Graphics, S.Osher and N.Paragios, Eds., Springer, (2003) 103-119. [13] Z. Wang, X. Tan and Qi Yu , Sparse PDE for SAR image speckle suppression, IET Journal of Image Processing, 11(6) (2017) 425-432. [14] G. Aubert and J. Aujol, A variational approach to removing multiplicative noise, SIAM J. Appl. Math., 68(4) (2008) 925-946. [15] Y. Huang, M. Ng and Y. Wen, A new total variation method for multiplicative noise removal, SIAM J. Imag. Sci.,2(1) (2009) 20-40. [16] J. M. Biocas-Dias and M. A. T. Figueiredo, Multiplicative noise removal using variable splitting and constrained optimization, IEEE Trans. Image Process., 19(7) (2010) 1720-1730. [17] Yi Cui, Guangyi Zhou, Jian Yang and Yoshio Yamaguchi, Unsupervised estimation of the equivalent number of looks in SAR images, IEEE Geoscience and Remote Sensing Letters, 8(4) (2011) 710-714. [18] D. Brunet, E.R. Vrscay and Z. Wang, On the mathematical properties of the structural similarty index, IEEE Transactions on Image Processing, 21(4) (2011) 1488-1499. [19] A. Achim, E. Kuruoglu and J. Zerubia, SAR image filtering based on the heavy-tailed Rayleigh model, Inst. Nat. Recherche Informat. Automatique (INRIA), France, Tech. Rep. 5493 (2005). [20] Q. Huynh-Thu, M. Ghanbari, Scope of validity of PSNR in image/video quality assessment, IET, Electronic Letters, 44(13) (2008) 800-801. [21] Haybert Markarian and Sedigheh Ghofrani, High-TV based CS framework using MAP estimator for SAR image enhancement, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9) (2017) 4059-4073. [22] A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms with a new one, SIAM Interdiscip. J. Multiscale Model. Simul., 4(2) (2005) 490-530. [23] L. Gomez et. al., Supervised constrained optimization of bayesian non-local means filter with sigma preselection for despeckling SAR images, IEEE Trans. Geosci. Remote Sens., 51(8) (2013) 4563-4575. [24] S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, A nonlocal SAR image denoising algorithm base on LLMMSE wavelet shrinkage, IEEE Trans. Geosci. Remote Sens., 50(2) (2012) 606-616. [25] L. Gomez, M. E. Buemi, J. C. Jacobo-Berlles and M. E. Mejail, A new image quality index for objectively evaluating despeckling filtering in SAR images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(3) (2016) 1297-1307. [26] Xial Guang, Jiang Le, Jin Huang and Liu Jun, A fast high-order total variation minimization method for multiplicative noise removal, Mathematical Problems in Engineering, 2013 (2013) 13pages Article ID: 834035. [27] R. Touzi, A. Lopes and P. Bousquet, A statistical and geometrical edge detector for SAR images, IEEE Trans. Image Process., 26(6) (1998) 764-773. [28] Y. Desnos and V. Matteini, Review on structure detection and speckle filtering on ERS-1 images, EARSeL adv. Remote Sens. 2(2) (1993) 52-65, 1993. [29] Matlab, The MathWorks, 2011. [30] Bin Xu, Yi Cui, Zenghui Li and Jian Yang, An iterative SAR image filtering method using nonlocal sparse model, IEEE Geoscience and Remote Sensing Letters,12(8) (2015) 1635-1639. [31] Bin Xu, Yi Cui, Zenghui Li, Bin Zuo, Jian Yang and Jianshe Song, Patch ordering based SAR image despeckling via transform domain filtering, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4) (2015) 1682-1695. [32] D. Cozzolino, S. Parrilli, G. Scarpa, G. Poggi and L. Verdoliva, Fast adaptive nonlocal SAR despeckling, IEEE Geoscience and Remote Sensing Letters, 11(2) (2014) 524-528. [33] I. G. Cumming and F. H. Wong, Digital Signal Processing of Synthetic Aperture Radar Data: Algorithms and Implementation: Norwood, MA, USA: Artech House, (2004). | ||
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