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حذف نویز ضربه‌ای از تصاویر دیجیتالی مبتنی بر تخمین توزیع مکانی نویزها | ||
| مدل سازی در مهندسی | ||
| مقاله 2، دوره 12، شماره 39، دی 1393، صفحه 13-29 اصل مقاله (1.84 M) | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2017.1688 | ||
| نویسندگان | ||
| اصغر زارع؛ علی محمدزاده* | ||
| دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| تاریخ دریافت: 09 بهمن 1395، تاریخ بازنگری: 27 شهریور 1396، تاریخ پذیرش: 09 بهمن 1395 | ||
| چکیده | ||
| هدف از این پژوهش ارائه روشی جهت شناسایی و حذف نویزهای تک ضربه‌ای روشن و تیره می‌باشد. در این پژوهش جهت شناسایی نویزهای تک‌ضربه‌ای در تصویر نویزدار دو الگوریتم آشکارساز معرفی شده است. روش پیشنهادی در این پژوهش یک پروسه تکراری می‌باشد که هر تکرار شامل سه مرحله است. روش ارائه شده می‌تواند بر مبنای هر دو الگوریتم آشکارساز معرفی شده جهت شناسایی نویزهای ضربه‌ای کار کند. در این تحقیق جهت بررسی روش پیشنهادی نویزهای تک ضربه‌ای با سه نوع توزیع متفاوت مدلسازی و به تصویر اصلی اضافه شده است و تصویر بازسازی شده با تصویر اصلی مورد مقایسه قرار گرفته است. جهت ارزیابی نویزهای شناسایی شده توسط الگوریتم‌های آشکارساز پیشنهادی و کیفیت تصاویر بازسازی شده به ترتیب از شاخص صحت کلی و شاخص نسبت اوج سیگنال به نویز استفاده شده است. نتایج ارزیابی‌ها نشان می‌دهند که روش پیشنهادی بر مبنای دو الگوریتم آشکارساز معرفی شده به مراتب بهتر از روش فیلترینگ دو عبوری میانه می‌باشد و نسبت به یکی از روش‌های معرفی شده قبلی یعنی فیلترینگ دو عبوری میانه توافقی نتایج بهتری ارائه داده است. کیفیت تصاویر بازسازی شده توسط روش پیشنهادی بیش‌تر از روش فیلترینگ دو عبوری میانه و دو عبوری میانه توافقی می‌باشد. این افزایش کیفیت بطور میانگین و به ترتیب معادل با افزایش 5/37 و 4/17 دسیبل در شاخص نسبت اوج سیگنال به نویز می‌باشد. | ||
| کلیدواژه‌ها | ||
| نویز تک‌ضربه‌ای؛ الگوریتم آشکارساز نویز؛ فیلترینگ دو عبوری میانه؛ فیلترینگ دو عبوری میانه توافقی؛ شاخص نسبت اوج سیگنال به نویز؛ شاخص صحت کلی | ||
| عنوان مقاله [English] | ||
| UniPolar impulse noise removing from digital images based on estimation of noises spatial distribution | ||
| نویسندگان [English] | ||
| asghar Zarea؛ Ali Mohammadzadeh | ||
| چکیده [English] | ||
| This article introduces a method to detect and remove unipolar impulse noises from digital images. In this study, proposed method is a repetitive procedure which each repeat contain three steps. Also, proposed method can be based on two detection algorithms that are introduced to detect unipolar impulse noises. In this research, unipolar impulse noises with 3 different distribution types are modeled and added to original image to produce noised image. Then, image is reconstructed based on proposed method from produced noised image. Finally, reconstructed image is evaluated in comparison to original image. Detected noises by introduced algorithms are evaluated by Overall Accuracy index. Also, quality of the reconstructed image is evaluated by Peak Signal to Noise Ratio (PSNR) index. Evaluation results are shown that proposed method based on two detection algorithms is better than regular and adaptive two-pass median filters. Quality of the reconstructed images by proposed method is high than results of regular and adaptive two-pass median filters. The average enhancement in quality is equivalent with increasement 5.37 and 4.17 decibels in PSNR index respectively. | ||
| کلیدواژه‌ها [English] | ||
| Unipolar Impulse Noise, Noise Detection Algorithm, Two-Pass Median Filter (TPMF), Adaptive Two-Pass Median Filter (ATPMF), Peak Signal to Noise Ratio (PSNR) Index, Overall Accuracy (OA) Index | ||
| مراجع | ||
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[1] Eng, H.L., Ma, K.K. (2001). “Noise adaptive soft-switching median filter”. IEEE Transactions on Image Processing, Vol. 10, No. 2, pp. 2542-251. [2] Astola, J. and Kuosmanen, P. (1977). “Fundamentals of Nonlinear Digital Filtering”. CRC Press. [3] Gong, B., Im, J., Mountrakis, G. (2011). “An artificial immune network approach to multi-sensor land use/land cover classification”. Remote Sensing Environment, Vol. 115, No. 2, pp. 600–614. [4] Naseem, I., Togneri, R., Bennamoun, M. (2012). “Robust regression for face recognition”. Pattern Recognition, Vol. 45, No. 1, pp. 104–118. [5] Luo, W. (2006). “An Efficient Detail-Preserving Approachfor Removing Impulse Noise in Images”. IEEE Signal Processing Letters, Vol. 13, No. 7, pp. 413-416. [6] Hsieh, M.H., Cheng, F.Ch., ChauShie, M., JangRuan, Sh. (2013). “Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images”. Engineering Applications of Artificial Intelligence, Vol. 26, No. 4, pp. 1333-1338. [7] Pitas, I. and Venetsanopoulos, A.N. (1990). “Nonlinear Digital Filters: Principles and Applications”. Kluwer, Norwell. [8] Zhang, X., Zhan, Y., Ding, M., Hou, W., Yin, Zh. (2013). “Decision-based non-local means filter for removing impulse noise from digital images”. Signal Processing, Volume 93, No. 2, pp. 517-524. [9] Jain, A.K. (1989). “Fundamentals of Digital Image Processing”. Prentice & Hall. [10] Arce, G.R. and Foster, R.E. (1989). “Detail-preserving ranked-order based filters for image processing”. IEEE Transaction on Acoust., Speech, Signal Processing, Vol. 37, No. 1, pp. 83–98. [11] Bovik, A.C., Huang T.S., and Munson, D.C. (1985). “Edge-sensitive image restoration using order-constrained least squares methods”. IEEE Transaction on Acoustics Speech Signal Processing, Vol. 33, No. 5, pp. 1253–1263. [12] Nodes, T.A. and Gallagher, N.C. (1984). “The output distribution of median type filters”. IEEE Transaction on Communications, Vol. 32, No. 5, pp. 532–541. [13] Bovik, A.C. (1985). “Streaking in median filtered images”. IEEE Transactions on Acoustics Speech Signal Processing, Vol. 35, No. 4, pp. 493–503. [14] Yang, R., Lin, L., Gabbouj, M., Astola, J. and Neuvo, Y. (1995). “Optimal weighted median filters under structural constraints”. IEEE Trans Signal Processing, Vol. 43, No. , pp. 591–604. [15] Chen, T. and Wu, H. (2001). “Adaptive impulse detection using center-weighted median filters”. Signal Processing Letters, Vol. 8, No. 1, pp. 1–3. [16] Song, T., Gabbouj, M., and Neuvo, Y. (1994). “Center weighted median filters: some properties and applications in image processing”. Signal Processing, Vol. 35, No. 3, pp. 213–229. [17] Ko, S.J. and Lee, Y.H. (1991). “Center weighted median filters and their applications to image enhancement”. IEEE Transactions on Circuits and Systems,Vol. 38, No. 9, pp.984–993. [18] Hardie, R.C., and Barner, K.E. (1994). “Rank conditioned rank selection filters for signal restoration”. IEEE Trans. Image Process., Vol. 3, No. 2, pp. 192–206. [19] Sun, T. and Neuvo, Y. (1994). “Detail-preserving median based filters in image processing”. Pattern Recognit. Letters, Vol. 15, No. 4, pp. 341–347. [20] Zhang, S., and Karim, M.A. (2002). “A new impulse detector for switching median filters”. IEEE Signal Processing Letters, Vol. 9, No. 11, pp. 360–363. [21] Florencio, D.A.F., and Shafer, R.W. (1994). “Decision-based median filter using local signal statistics”. Visual Communication and Image Processing 94, Chicago, Illinois, United States, 25 September. [22] Zhang, D. and Wang, Z. (1997). “Impulse noise detection and removal using fuzzy techniques”. IEE Electronics Letters, Vol. 33, No. 5, pp. 378–379. [23] Russo, F., and Ramponi, G. (1996). “A fuzzy filter for images corrupted by impulse noise”. IEEE Signal Processing Letters, Vol. 3, No. 6, pp. 168–170. [24] Abreu, E., Lightstone, M., Mitra, S.K., and Arakawa, K. (1996). “A new efficient approach for the removal of impulse noise from highly corrupted images”. IEEE Transactions on Image Processing, Vol. 5, No. 6, pp. 1012–1025. [25] Hwang, H., and Haddad, R.A. (1995). “Adaptive median filters: Newalgorithms and results”. IEEE Transactions on Signal Processing, Vol. 4, No. 4, pp. 499–502. [26] Lin, H., and Willson, A.N. (1988). “Median filter with adaptive length”. IEEE Transactions on Circuits and Systems, Vol. 35, No. 6, pp. 675–690. [27] Chen, T., Ma, K., and Chen, L. (1999). “Tri-state median filter for image denoising”. IEEE Transaction on Image Processing, Vol. 8, No. 12, pp. 1834–1838. [28] Xu, X., Miller, E.L., Chen, D., and Sarhadi, M. (2004). “Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images”. IEEE Transactions On Image Processing, Vol. 13, No. 2. [29] Dang, D., Luo, W. (2007). “Impulse noise removal utilizing second-order difference analysis”. Signal Processing, Vol. 87, No. 9, pp. 2017-2025. [30] Yuan, S.Q., Tan, Y.H. (2006). “Impulse noise removal by a global–local noise detector and adaptive median filter”. Signal Processing, Vol. 86, No. 8, pp. 2123-2128. [31] Deng, Z.F., Yin, Z.P., Xiong, Y.L. (2007). “High probability impulse noise- removing algorithm based on mathematical morphology”. IEEE Signal Processing Letters, Vol. 14, No. 1, pp. 31–34. [32] Srinivasan, K.S., Ebenezer, D. (2007). “A new fast and efficient decision-based algorithm for removal of high-density impulse noises”. IEEE Signal Processing Letters, Vol. 14, No. 3, pp. 189-192. [33] Chen, P.Y., Lien, C.Y. (2008). “An efficient edge-preserving algorithm for removal of salt-and-pepper noise”. IEEE Signal Processing Letters, Vol. 15, pp. 833-836. [34] Zhang, X.M., Xiong, Y.L. (2009). “Impulse noise removal using directional weighted noise detector and adaptive weighted mean filter”. IEEE Signal Processing Letters, Vol. 16, No. 4, pp. 295-298. [35] Wang, Z., and Zhang, D. (1999). “Progressive switching median filter for removal of impulsive noise from highly corrupted image”. IEEE Transaction on Circuits and Systems II, Analog and Digitital Signal Processing, Vol. 46, No. 1, pp. 78-80. [36] Gonzalez, R.C., Woods, R.E., (2002). “Digital Image Processing”. Second Edition, Prentice & Hall Inc. [37] Sampath, A., and Shan, J. (2004). “Urban modelling based on segmentation and regularization of airborne lidar point clouds”. ISPRS Archives, Istanbul, Turkey, 12-23 July. [38] Shan, J., and Sampath, A. (2005). “Urban dem generation from raw lidar data: a labeling algorithm and its performance”. Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 02, pp. 217–226. [39] Khoshelham, K., Nardinocchi, C., Frontoni, E., Mancini, A., and Zingaretti, P. (2011). “Performance evaluation of automated approaches to building detection in multi-source aerial data”. ISPRS Journal of Photogrammetry and Remote Sensing, Vol.65, No.1, pp.123-133. [40] Salamon, D. (2007). “Data Compression: The complete Reference”. Fourth Edition, Springer-Verlag, London. [41] Salamon, D. (2005). “Coding for Data and Comupter Communications”. Springer.
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