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Periodic Noise Types and Structural Properties

Yıl 2022, Cilt: 15 Sayı: 2, 140 - 150, 15.12.2022

Murat Altunok , Bülent Turan

https://doi.org/10.54525/tbbmd.1120453

Öz

Periodic noises reduce image quality by superimposing similar patterns. This noise appears as peaks in the frequency space. There are general and specific periodic noise removal methods in the literature. It is important to know the type of periodic noise that occurs in order to clear the noises. In this research, a study was carried out to examine and create periodic noise types, to add the generated periodic noises to the image and to determine the peaks in the image spectrum as a result of addition. Since there is no study that specifically examines the periodic noise types when the literature is scanned, our study aims to meet this need.

Anahtar Kelimeler

periodic noise low-high-multi frequency periodic noise pure periodic noise semi-periodic noise stripe noise periodic noise,

Kaynakça

  • Abu-Ein, Ashraf Abdel-Karim Helal. A novel methodology for digital removal of periodic noise using 2D fast Fourier transforms. Contemporary Engineering Science 3 (2014): 103-116.
  • Laus, Friederike, Fabien Pierre, and Gabriele Steidl. Nonlocal myriad filters for Cauchy noise removal. Journal of Mathematical Imaging and Vision 60.8 (2018): 1324-1354.
  • Moallemi, Payman, and Majid Behnampourii. Adaptive optimum notch filter for periodic noise reduction in digital images. AUT Journal of Electrical Engineering 42.1 (2010): 1-7.
  • Driggers, Ronald G., Melvin H. Friedman, and Jonathan Nichols. Introduction to infrared and electro-optical systems. Artech House, 2012.
  • Boyat, Ajay Kumar, and Brijendra Kumar Joshi. A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489 (2015).
  • Guttman, Newman, and Bela Julesz. Lower limits of auditory periodicity analysis. The Journal of the Acoustical Society of America 35.4 (1963): 610-610.
  • J. Wang and D. C. Liu, 2-D FFT for Periodic Noise Removal on Strain Image & &, 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010, pp. 1-4, doi: 10.1109/ICBBE.2010.5517762.
  • Carvalho, Ângela, et al. MobilityAnalyser: A novel approach for automatic quantification of cell mobility on periodic patterned substrates using brightfield microscopy images. Computer methods and programs in biomedicine 162 (2018): 61-67.
  • Chen, Yong, et al. Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint. Remote sensing 9.6 (2017): 559.
  • Xie, Yijing, L. Chen, and U. G. Hofmann. Reduction of periodic noise in Fourier domain optical coherence tomography images by frequency domain filtering. Biomedical Engineering/Biomedizinische Technik 57.SI-1-Track-P (2012): 830-832.
  • Schowengerdt, Robert A. Remote sensing: models and methods for image processing. Elsevier, 2006.
  • X. Liu, X. Lu, H. Shen, Q. Yuan, Y. Jiao and L. Zhang, Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties, in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 3049-3060, May 2016, doi: 10.1109/TGRS.2015.2510418.
  • Wei, Zhouping, et al. A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image. Micron 43.2-3 (2012): 170-176.
  • Ionita, Marius G., and Henri George Coanda. Automatic periodic noise removal in microscopy images. 2017 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2017.
  • Erol, Asiye, Meliha Ece Gürbüz, and Ali Gangal. Video görüntülerindeki periyodik gürültülerin yok edilmesi. (2006).
  • Kang, Xiangui, et al. Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Transactions on Information Forensics and Security 7.2 (2011): 393-402.
  • Varghese, Justin. Adaptive threshold based frequency domain filter for periodic noise reduction. AEU-international journal of electronics and communications 70.12 (2016): 1692-1701.
  • Aizenberg, Igor N., and Constantine Butakoff. Frequency domain medianlike filter for periodic and quasi-periodic noise removal. Image Processing: Algorithms and Systems. Vol. 4667. International Society for Optics and Photonics, 2002.
  • Moallem, Payman, Monire Masoumzadeh, and Mehdi Habibi. A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image. Signal, image and video processing 9.5 (2015): 1179-1191.
  • Novikov, Anatoly, Anton Pronkin, and Sergey Vityazev. Edge Detector Application in the Problem of Periodic Interference Filtering. 2022 24th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2022.
  • Dutta, Souradeep, et al. Periodic noise recognition and elimination using RFPCM clustering. 2014 International Conference on Electronics and Communication Systems (ICECS). IEEE, 2014.
  • Aizenberg, Igor, and Constantine Butakoff. A windowed Gaussian notch filter for quasi-periodic noise removal. Image and Vision Computing 26.10 (2008): 1347-1353.
  • Gonzalez, Rafael C. Digital image processing. Pearson education india, 2009.
  • Hiremath, Shilpa, and A. Shobha Rani. A Concise Report on Image Types, Image File Format and Noise Model for Image Preprocessing. (2020).
  • Ketenci, Seniha, and Ali Gangal. Design of Gaussian star filter for reduction of periodic noise and quasi-periodic noise in gray level images. 2012 International Symposium on Innovations in Intelligent Systems and Applications. IEEE, 2012.
  • Varghese, Justin, et al. Laplacian-based frequency domain filter for the restoration of digital images corrupted by periodic noise. Canadian journal of electrical and computer engineering 39.2 (2016): 82-91.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Self-learning based image decomposition for blind periodic noise estimation: a dual-domain optimization approach. Multidimensional Systems and Signal Processing 32.2 (2021): 465-490.
  • Alibabaie, Najmeh, and AliMohammad Latif. Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform. Journal of Mathematical Imaging and Vision 63.4 (2021): 503-527.
  • Leventhall, Geoff. Low Frequency Noise. What we know, what we do not know, and what we would like to know. Journal of Low Frequency Noise, Vibration and Active Control 28.2 (2009): 79-104.
  • Rieken, David W. Periodic noise in very low frequency power-line communications. 2011 IEEE International Symposium on Power Line Communications and Its Applications. IEEE, 2011.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Fuzzy Notch Filter for Periodic and Quasi-Periodic Noise Reduction in Digital Images. Journal of Machine Vision and Image Processing (2019).
  • Sur, Frédéric, and Michel Grediac. An automated approach to quasi-periodic noise removal in natural images. Diss. INRIA Nancy, équipe Magrit; Institut Pascal, Université Blaise Pascal; INRIA, 2015.
  • Chakraborty, Debolina, et al. Gabor-based spectral domain automated notch-reject filter for quasi-periodic noise reduction from digital images. Multimedia tools and applications 78.2 (2019): 1757-1783.
  • Ketenci, Seniha, and Ali Gangal. Automatic reduction of periodic noise in images using adaptive Gaussian star filter. Turkish Journal of Electrical Engineering and Computer Sciences 25.3 (2017): 2336-2348.
  • N. Acito, M. Diani and G. Corsini, Subspace-Based Striping Noise Reduction in Hyperspectral Images, in IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 4, pp. 1325-1342, April 2011, doi: 10.1109/TGRS.2010.2081370.
  • Tsai, Fuan, and Walter W. Chen. Striping noise detection and correction of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 46.12 (2008): 4122-4131.
  • Fehrenbach, Jérôme, Pierre Weiss, and Corinne Lorenzo. Variational algorithms to remove stationary noise: applications to microscopy imaging. IEEE transactions on image processing 21.10 (2012): 4420-4430.
  • Iordache, Marian-Daniel, José M. Bioucas-Dias, and Antonio Plaza. Sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 49.6 (2011): 2014-2039.
  • Ghamisi, Pedram, Mauro Dalla Mura, and Jon Atli Benediktsson. A survey on spectral–spatial classification techniques based on attribute profiles. IEEE Transactions on Geoscience and Remote Sensing 53.5 (2014): 2335-2353.
  • Cao, Wenfei, et al. Destriping remote sensing image via low-rank approximation and nonlocal total variation. IEEE Geoscience and Remote Sensing Letters 15.6 (2018): 848-852.
  • Cao, Chunhong, et al. Anisotropic total variation model for removing oblique stripe noise in remote sensing image. Optik 227 (2021): 165254.
  • Chang, Yi, et al. Remote sensing image stripe noise removal: From image decomposition perspective. IEEE Transactions on Geoscience and Remote Sensing 54.12 (2016): 7018-7031.
  • Guan, Juntao, Rui Lai, and Ai Xiong. Wavelet deep neural network for stripe noise removal. IEEE Access 7 (2019): 44544-44554.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Bio-inspired Computing Paradigm for Periodic‎ Noise Reduction in Digital Images. Journal of AI and Data Mining 9.1 (2021): 19-29.
  • Abolhassani, M. Formulation of moiré fringes based on spatial averaging. Optik 122.6 (2011): 510-513.
  • Yu, Yongjian, and Jue Wang. A novel grid regression demodulation method for radiographic grid artifact correction. Medical Physics 48.7 (2021): 3790-3803.
  • Mars yüzey görüntüsü, (https://nssdc.gsfc.nasa.gov/).
  • Saf periyodik gürültüye sahip görüntü (https://petermasek.tripod.com/).
  • Saf periyodik gürültü içerek görüntü (http://scanlines.hazard-city.de/).
  • Chakraborty, D. Ve ark. A proficient method for periodic and quasi-periodic noise fading using spectral histogram thresholding with sinc restoration filter. AEU-international journal of electronics and communications 70.12 (2016): 1580-1592.

Periyodik Gürültü Tipleri ve Yapısal Özellikleri

Yıl 2022, Cilt: 15 Sayı: 2, 140 - 150, 15.12.2022

Murat Altunok , Bülent Turan

https://doi.org/10.54525/tbbmd.1120453

Öz

Periyodik gürültüler, benzer desenleri üst üste bindirerek görüntü kalitesini düşürür. Bu gürültü, frekans uzayında tepe noktaları olarak görünür. Literatürde genel ve belli periyodik gürültü temizleme yöntemleri mevcuttur. Gürültülerin temizlenebilmesi için oluşan periyodik gürültünün tipinin bilinmesi önemlidir. Bu araştırmada periyodik gürültü tiplerinin incelenmesi, oluşturulması, oluşturulan periyodik gürültülerin görüntü üzerine eklenmesi ve eklenme sonucunda görüntü spektrumunda tepe noktalarının belirlenmesi üzerine bir çalışma gerçekleştirilmiştir. Literatür tarandığında periyodik gürültü tiplerinin belli olarak inceleyen bir çalışma olmamasından dolayı çalışmamız bu ihtiyacı giderme amaçlıdır.

Anahtar Kelimeler

periyodik gürültü sentetik periyodik gürültü düşük-yüksek-çok frekanslı periyodik gürültü saf periyodik gürültü yarı periyodik gürültü şerit (sıyırma) gürültüsü.

Kaynakça

  • Abu-Ein, Ashraf Abdel-Karim Helal. A novel methodology for digital removal of periodic noise using 2D fast Fourier transforms. Contemporary Engineering Science 3 (2014): 103-116.
  • Laus, Friederike, Fabien Pierre, and Gabriele Steidl. Nonlocal myriad filters for Cauchy noise removal. Journal of Mathematical Imaging and Vision 60.8 (2018): 1324-1354.
  • Moallemi, Payman, and Majid Behnampourii. Adaptive optimum notch filter for periodic noise reduction in digital images. AUT Journal of Electrical Engineering 42.1 (2010): 1-7.
  • Driggers, Ronald G., Melvin H. Friedman, and Jonathan Nichols. Introduction to infrared and electro-optical systems. Artech House, 2012.
  • Boyat, Ajay Kumar, and Brijendra Kumar Joshi. A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489 (2015).
  • Guttman, Newman, and Bela Julesz. Lower limits of auditory periodicity analysis. The Journal of the Acoustical Society of America 35.4 (1963): 610-610.
  • J. Wang and D. C. Liu, 2-D FFT for Periodic Noise Removal on Strain Image & &, 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010, pp. 1-4, doi: 10.1109/ICBBE.2010.5517762.
  • Carvalho, Ângela, et al. MobilityAnalyser: A novel approach for automatic quantification of cell mobility on periodic patterned substrates using brightfield microscopy images. Computer methods and programs in biomedicine 162 (2018): 61-67.
  • Chen, Yong, et al. Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint. Remote sensing 9.6 (2017): 559.
  • Xie, Yijing, L. Chen, and U. G. Hofmann. Reduction of periodic noise in Fourier domain optical coherence tomography images by frequency domain filtering. Biomedical Engineering/Biomedizinische Technik 57.SI-1-Track-P (2012): 830-832.
  • Schowengerdt, Robert A. Remote sensing: models and methods for image processing. Elsevier, 2006.
  • X. Liu, X. Lu, H. Shen, Q. Yuan, Y. Jiao and L. Zhang, Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties, in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 3049-3060, May 2016, doi: 10.1109/TGRS.2015.2510418.
  • Wei, Zhouping, et al. A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image. Micron 43.2-3 (2012): 170-176.
  • Ionita, Marius G., and Henri George Coanda. Automatic periodic noise removal in microscopy images. 2017 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2017.
  • Erol, Asiye, Meliha Ece Gürbüz, and Ali Gangal. Video görüntülerindeki periyodik gürültülerin yok edilmesi. (2006).
  • Kang, Xiangui, et al. Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Transactions on Information Forensics and Security 7.2 (2011): 393-402.
  • Varghese, Justin. Adaptive threshold based frequency domain filter for periodic noise reduction. AEU-international journal of electronics and communications 70.12 (2016): 1692-1701.
  • Aizenberg, Igor N., and Constantine Butakoff. Frequency domain medianlike filter for periodic and quasi-periodic noise removal. Image Processing: Algorithms and Systems. Vol. 4667. International Society for Optics and Photonics, 2002.
  • Moallem, Payman, Monire Masoumzadeh, and Mehdi Habibi. A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image. Signal, image and video processing 9.5 (2015): 1179-1191.
  • Novikov, Anatoly, Anton Pronkin, and Sergey Vityazev. Edge Detector Application in the Problem of Periodic Interference Filtering. 2022 24th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2022.
  • Dutta, Souradeep, et al. Periodic noise recognition and elimination using RFPCM clustering. 2014 International Conference on Electronics and Communication Systems (ICECS). IEEE, 2014.
  • Aizenberg, Igor, and Constantine Butakoff. A windowed Gaussian notch filter for quasi-periodic noise removal. Image and Vision Computing 26.10 (2008): 1347-1353.
  • Gonzalez, Rafael C. Digital image processing. Pearson education india, 2009.
  • Hiremath, Shilpa, and A. Shobha Rani. A Concise Report on Image Types, Image File Format and Noise Model for Image Preprocessing. (2020).
  • Ketenci, Seniha, and Ali Gangal. Design of Gaussian star filter for reduction of periodic noise and quasi-periodic noise in gray level images. 2012 International Symposium on Innovations in Intelligent Systems and Applications. IEEE, 2012.
  • Varghese, Justin, et al. Laplacian-based frequency domain filter for the restoration of digital images corrupted by periodic noise. Canadian journal of electrical and computer engineering 39.2 (2016): 82-91.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Self-learning based image decomposition for blind periodic noise estimation: a dual-domain optimization approach. Multidimensional Systems and Signal Processing 32.2 (2021): 465-490.
  • Alibabaie, Najmeh, and AliMohammad Latif. Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform. Journal of Mathematical Imaging and Vision 63.4 (2021): 503-527.
  • Leventhall, Geoff. Low Frequency Noise. What we know, what we do not know, and what we would like to know. Journal of Low Frequency Noise, Vibration and Active Control 28.2 (2009): 79-104.
  • Rieken, David W. Periodic noise in very low frequency power-line communications. 2011 IEEE International Symposium on Power Line Communications and Its Applications. IEEE, 2011.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Fuzzy Notch Filter for Periodic and Quasi-Periodic Noise Reduction in Digital Images. Journal of Machine Vision and Image Processing (2019).
  • Sur, Frédéric, and Michel Grediac. An automated approach to quasi-periodic noise removal in natural images. Diss. INRIA Nancy, équipe Magrit; Institut Pascal, Université Blaise Pascal; INRIA, 2015.
  • Chakraborty, Debolina, et al. Gabor-based spectral domain automated notch-reject filter for quasi-periodic noise reduction from digital images. Multimedia tools and applications 78.2 (2019): 1757-1783.
  • Ketenci, Seniha, and Ali Gangal. Automatic reduction of periodic noise in images using adaptive Gaussian star filter. Turkish Journal of Electrical Engineering and Computer Sciences 25.3 (2017): 2336-2348.
  • N. Acito, M. Diani and G. Corsini, Subspace-Based Striping Noise Reduction in Hyperspectral Images, in IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 4, pp. 1325-1342, April 2011, doi: 10.1109/TGRS.2010.2081370.
  • Tsai, Fuan, and Walter W. Chen. Striping noise detection and correction of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 46.12 (2008): 4122-4131.
  • Fehrenbach, Jérôme, Pierre Weiss, and Corinne Lorenzo. Variational algorithms to remove stationary noise: applications to microscopy imaging. IEEE transactions on image processing 21.10 (2012): 4420-4430.
  • Iordache, Marian-Daniel, José M. Bioucas-Dias, and Antonio Plaza. Sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 49.6 (2011): 2014-2039.
  • Ghamisi, Pedram, Mauro Dalla Mura, and Jon Atli Benediktsson. A survey on spectral–spatial classification techniques based on attribute profiles. IEEE Transactions on Geoscience and Remote Sensing 53.5 (2014): 2335-2353.
  • Cao, Wenfei, et al. Destriping remote sensing image via low-rank approximation and nonlocal total variation. IEEE Geoscience and Remote Sensing Letters 15.6 (2018): 848-852.
  • Cao, Chunhong, et al. Anisotropic total variation model for removing oblique stripe noise in remote sensing image. Optik 227 (2021): 165254.
  • Chang, Yi, et al. Remote sensing image stripe noise removal: From image decomposition perspective. IEEE Transactions on Geoscience and Remote Sensing 54.12 (2016): 7018-7031.
  • Guan, Juntao, Rui Lai, and Ai Xiong. Wavelet deep neural network for stripe noise removal. IEEE Access 7 (2019): 44544-44554.
  • Alibabaie, Najmeh, and Ali Mohammad Latif. Bio-inspired Computing Paradigm for Periodic‎ Noise Reduction in Digital Images. Journal of AI and Data Mining 9.1 (2021): 19-29.
  • Abolhassani, M. Formulation of moiré fringes based on spatial averaging. Optik 122.6 (2011): 510-513.
  • Yu, Yongjian, and Jue Wang. A novel grid regression demodulation method for radiographic grid artifact correction. Medical Physics 48.7 (2021): 3790-3803.
  • Mars yüzey görüntüsü, (https://nssdc.gsfc.nasa.gov/).
  • Saf periyodik gürültüye sahip görüntü (https://petermasek.tripod.com/).
  • Saf periyodik gürültü içerek görüntü (http://scanlines.hazard-city.de/).
  • Chakraborty, D. Ve ark. A proficient method for periodic and quasi-periodic noise fading using spectral histogram thresholding with sinc restoration filter. AEU-international journal of electronics and communications 70.12 (2016): 1580-1592.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler(Derleme)
Yazarlar

Murat Altunok 0000-0002-2756-2586

Bülent Turan 0000-0003-0673-469X

Erken Görünüm Tarihi 3 Aralık 2022
Yayımlanma Tarihi 15 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 15 Sayı: 2

Kaynak Göster

APA Altunok, M., & Turan, B. (2022). Periyodik Gürültü Tipleri ve Yapısal Özellikleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 15(2), 140-150. https://doi.org/10.54525/tbbmd.1120453
AMA Altunok M, Turan B. Periyodik Gürültü Tipleri ve Yapısal Özellikleri. TBV-BBMD. Aralık 2022;15(2):140-150. doi:10.54525/tbbmd.1120453
Chicago Altunok, Murat, ve Bülent Turan. “Periyodik Gürültü Tipleri Ve Yapısal Özellikleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 15, sy. 2 (Aralık 2022): 140-50. https://doi.org/10.54525/tbbmd.1120453.
EndNote Altunok M, Turan B (01 Aralık 2022) Periyodik Gürültü Tipleri ve Yapısal Özellikleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 15 2 140–150.
IEEE M. Altunok ve B. Turan, “Periyodik Gürültü Tipleri ve Yapısal Özellikleri”, TBV-BBMD, c. 15, sy. 2, ss. 140–150, 2022, doi: 10.54525/tbbmd.1120453.
ISNAD Altunok, Murat - Turan, Bülent. “Periyodik Gürültü Tipleri Ve Yapısal Özellikleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 15/2 (Aralık 2022), 140-150. https://doi.org/10.54525/tbbmd.1120453.
JAMA Altunok M, Turan B. Periyodik Gürültü Tipleri ve Yapısal Özellikleri. TBV-BBMD. 2022;15:140–150.
MLA Altunok, Murat ve Bülent Turan. “Periyodik Gürültü Tipleri Ve Yapısal Özellikleri”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, c. 15, sy. 2, 2022, ss. 140-5, doi:10.54525/tbbmd.1120453.
Vancouver Altunok M, Turan B. Periyodik Gürültü Tipleri ve Yapısal Özellikleri. TBV-BBMD. 2022;15(2):140-5.
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