حذف نویز سریع در تصویر SAR با استفاده از نمایش تنک

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده فنی مهندسی - دانشگاه دامغان

2 دانشکده مهندسی - دانشگاه فردوسی مشهد

چکیده

یکی از عوامل مخرب در تشخیص جزئیات صحنه از تصاویر رادار دهانه مصنوعی، وجود نویز لکه در تصاویر است. روش‌های مختلفی در حذف نویز تصویر با استفاده از تکنیک نمایش تنک ارائه شده‌اند که با حفظ جزئیات، نویز تصویر را به‌شکل مناسبی حذف می‌کنند. با توجه به حجم محاسبات این روش‌ها و ابعاد بزرگ تصاویر SAR، استفاده از آن‌ها در تصاویر SAR چالش‌برانگیز است. در این مقاله، روشی برای حذف نویز از تصاویر SAR، با استفاده از نمایش تنک ارائه شده که در آن، با حفظ کیفیت تصویر، زمان اجرا کاهش یافته است. در این روش، حذف نویز در دو مرحله انجام می‌شود. در مرحله اول، ابتدا تصویر به کمک یک روش ساده حذف نویز اولیه می‌شود و در مرحله بعد، جزئیات حذف‌شده، به‌کمک تکنیک نمایش تنک بازیابی شده و به تصویر اضافه می‌شود. با توجه به اینکه در مرحله بازیابی، نیازی به باز پردازش نواحی همگن تصویر وجود ندارد، تنها جزئیات نواحی ناهمگن تصویر بازیابی می‌شود و با استفاده از ماتریس نمونه‌بردار تصادفی در فرایند بازسازی تصویر، حجم پردازش در استفاده از این تکنیک، کاهش می‌یابد. نتایج شبیه‌سازی نشان می‌دهد این روش می‌تواند تصویری با کیفیت بهتر را در زمان حدود 0.2 روش‌های قبل ایجاد کند.  

کلیدواژه‌ها

عنوان مقاله [English]

High-speed Noise Reduction in SAR Images Using Sparse Representation

نویسندگان [English]

  • J. abbasi aghamaleki 1
  • M. M. Mousavi Shushtari 2
1 Faculty of Engineering Department, Damghan University, Damghan, Iran
2 Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

One of the destructive factors in denotation scene details from the Synthetic Aperture radar images is the presence of speckle noise in it. Different method of image denoising have been proposed using the Sparse Representation (SR) Technique, which, eliminates the noise of the image properly by preserving details. Because of the Computational Complexity of these methods and the large dimensions of SAR images, their use in SAR images is challenging. This paper presents a new method in SAR image denoising by SR that reduce run time with preservation of image quality. In this method, the denoising performs in two step. Firstly, the image is filtered using a simple denoising method, and the removed details is then retrieved using the SR technique and added to the filtered image. By retrieving details from the heterogeneous regions of the image and using random sampling matrix in reconstruction image, the processing volume and the required memory in using SR technique are reduced. Simulation results show that this method, with preservation of image quality, has an operating time of about 0.2 of other methods.

کلیدواژه‌ها [English]

  • Synthetic aperture radar (SAR)
  • Sparse representation (SR)
  • Denoising
  • Dictionary learning

مراجع

[1] A. Moreira, “Synthetic aperture radar (SAR): principles and applications,” 4th Advanced Training Course in Land Remote Sensing, 2013.
[2] T. Lillesand, R. W. Kiefer, and J. Chipman, Remote sensing and image interpretation: John Wiley & Sons, 2014.
[3] T. K. Sjogren, V. T. Vu, M. I. Pettersson, A. Gustavsson and L. M. Ulander, “Moving target relative speed estimation and refocusing in synthetic aperture radar images,” IEEE Transactions on Aerospace and electronic systems, vol. 48, pp. 2426-2436, 2012.
 [4] A. Schmitt, “Multiscale and Multidirectional Multilooking for SAR Image Enhancement,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 5117-5134, 2016.
[5] R.L.Morrison, M.N. Do and D. C. Munson, “SAR image autofocus by sharpness optimization: A theoretical study,” IEEE Transactions on Image Processing, vol. 16, pp. 2309-2321, 2007.
[6] A.Shafiei, E.Yazdani and M.Beheshti, “SAR Speckle Reduction and Image Reconstruction Using Compressed,” Journal of Radar, vol. 4, no.2, pp.19-29, 2016.
[7] A. Lopes, R. Touzi, and E. Nezry, “Adaptive speckle filters and scene heterogeneity,” IEEE transactions on Geoscience and Remote Sensing, vol. 28, pp. 992-1000, 1990.
[8] D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 35, pp. 373-383, 1987.
[9] V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Transactions on pattern analysis and machine intelligence, pp. 157-166, 1982
[10] A. Lopes, E. Nezry, R. Touzi and H. Laur, “Maximum a posteriori speckle filtering and first order texture models in SAR images,” 10th Geoscience and Remote Sensing Symposium,pp. 2409-2412, 1990.
[11] W. Zhang, F. Liu, and L. Jiao, “SAR image despeckling via bilateral filtering,” Electronics letters, vol.45, pp. 781-783, 2009.
[12] J. Zhu, J. Wen and Y. Zhang, “A new algorithm for SAR image despeckling using an enhanced Lee filter and median filter,” 6th Image and Signal Processing (CISP), pp. 224-228, 2013.
[13] Li, C.-L. Wang, P.-P. Huang and W.-D. Yu, “SAR image despeckling using a space-domain filter with alterable window,” IEEE Geoscience and Remote Sensing Letters, vol. 10, pp. 263-267, 2013.
[14] C.-A. Deledalle, L. Denis, and F. Tupin, “Iterative weighted maximum likelihood denoising with probabilistic patch-based weights,” IEEE Transactions on Image Processing, vol. 18, pp. 2661-2672, 2009.
[15] J. Chen, Y. Chen, W. An, Y. Cui, and J. Yang, “Nonlocal filtering for polarimetric SAR data: A pretest approach,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 1744-1754, 2011.
[16] M. H. Alkinani and M. R. El-Sakka, “Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction,” EURASIP Journal on Image and Video Processing, vol. 1, p. 58, 2017.
[17] J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman, “Non-local sparse models for image restoration,” 12th International Conference on IEEE Computer Vision, pp. 2272-2279, 2009.
[18] F. Argenti and L. Alparone, “Speckle removal from SAR images in the undecimated wavelet domain,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 2363-2374, 2002.
[19] S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, “A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, pp. 606-616, 2012.
]20[ کوشش منیره و اکبری زاده غلامرضا, “الگوریتم حذف Speckle با قابلیت حفظ لبه برای تصاویر سنجش از دور رادار روزنه ترکیبی با استفاده از تبدیل چند مقیاسه Curvelet و آستانه گذاری وفقی”, مجله مهندسی برق دانشگاه تبریز, جلد 45 , شماره 4, 153-161, زمستان 1394.
 [21] L. Gagnon and A. Jouan, “Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters,” in Wavelet Applications in Signal and Image Processing, pp. 80-92, 1997.
[22] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image processing, vol. 15, pp. 3736-3745, 2006.
[23] H. Rabbani and S. Gazor, “Image denoising employing local mixture models in sparse domains,” IET Image Processing, vol. 4, pp. 413-428, 2010.
[24] D.-A. Huang, L.-W. Kang, Y.-C. F. Wang, and C.-W. Lin, “Self-learning based image decomposition with applications to single image denoising,” IEEE Transactions on multimedia, vol. 16, pp. 83-93, 2014.
[25] C.W. Sang and H. Sun, “Two-Step Sparse Decomposition for SAR Image Despeckling,” IEEE Geoscience and Remote Sensing Letters, pp.1263-1267,2017.
[26] D. L. Donoho and M. Elad, “Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization,” Proceedings of the National Academy of Sciences, vol. 100, pp. 2197-2202, 2003.
[27] Gribonval, Rémi and Morten Nielsen. “Sparse representations in unions of bases.” IEEE transactions on Information theory vol49, pp 3320-3325,2003.
[28] T. Lu, S. Li, L. Fang, and J. A. Benediktsson, “SAR Image Despeckling Via Structural Sparse Representation,” Sensing and Imaging, vol. 17, p. 2, 2016.
[29] H. Xie, L. E. Pierce, and F. T. Ulaby, “Statistical properties of logarithmically transformed speckle,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 721-727, 2002.
[30] A. Eftekhari, M. Babaie-Zadeh and H. A. Moghaddam, “Two-dimensional random projection,” Signal processing, vol. 91, pp. 1589-1603, 2011.
[31] W.Qiu, E.Giusti, A.Bacci, M. Martorella, F. Berizzi, H. Zhao, et al., “Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, pp. 2166-2180, 2015.

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