Skip to main content
Erschienen in:

11.12.2023

A Modified Anisotropic Diffusion Scheme for Signal-Dependent Noise Filtering

verfasst von: Mariem Ben Abdallah, Jihene Malek, Abdullah Bajahzar, Hafedh Belmabrouk

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 4/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik. 

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information. 

Order your 30-days-trial for free and without any commitment.

Weitere Produktempfehlungen anzeigen
Literatur
  1. V.V. Abramova, S.K. Abramov, V.V. Lukin et al., On required accuracy of mixed noise parameter estimation for image enhancement via denoising. J. Image Video Proc. (2014). https://​doi.​org/​10.​1186/​1687-5281-2014-3View Article
  2. S. Abramov, V. Zabrodina, V. Lukin, B. Vozel, K. Chehdi, J. Astola, Improved method for blind estimation of the variance of mixed noise using weighted LMS line fitting algorithm, in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, pp. 2642–2645 (2010). https://​doi.​org/​10.​1109/​ISCAS.​2010.​5537084.
  3. H. Aetesam, S.K. Maji, Noise dependent training for deep parallel ensemble denoising in magnetic. Biomed. Signal Process. Control (2021). https://​doi.​org/​10.​1016/​j.​bspc.​2020.​102405.​66(4)View Article
  4. C. Aguerrebere, J. Delon, Y. Gousseau, P. Musé, Study of the digital camera acquisition process and statistical modeling of the sensor raw data. hal-00733538, (2013).
  5. B. Aiazzi, L. Alparone, S. Baronti, M. Selva, L. Stefani, Unsupervised estimation of signal-dependent CCD camera noise. EURASIP J. Adv. 231, 1–11 (2012)
  6. S. Aja-Fernandez, C. Alberola-Lopez, On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image Process. 15(9), 2694–2701 (2006). https://​doi.​org/​10.​1109/​TIP.​2006.​877360View Article
  7. S. Aja-Fernandez, Detail preserving anosotropic diffusion for speckle filtering (DPAD) (https://​www.​mathworks.​com/​matlabcentral/​fileexchange/​36906-detail-preserving-anosotropic-diffusion-for-speckle-filtering-dpad), MATLAB Central File Exchange. Retrieved September 21, (2023).
  8. P. Arbelaez, C. F. Retrieved from The Berkeley Segmentation Dataset and Benchmark: https://​www2.​eecs.​berkeley.​edu/​Research/​Projects/​CS/​vision/​bsds/​ (2007).
  9. C. Arboleda, Z. Wang, M. Stampanoni, Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography. Opt. Express 21(9), 10572–10589 (2013). https://​doi.​org/​10.​1364/​OE.​21.​010572View Article
  10. L. Ayala-Domínguez, R.M. Oliver, L.A. Medina, M.-E. Brandan, Design of a bilateral filter for noise reduction in contrast-enhanced micro-computed tomography. AIP Conf. Proc. 2348, 040002 (2021). https://​doi.​org/​10.​1063/​5.​0051272View Article
  11. L. Azzari, A. Foi, Gaussian–Cauchy mixture modeling for robust signal-dependent noise estimation, in IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), p. 5357–5361 (2014).
  12. M. Baraka, Nonlinear anisotropic diffusion methods for image denoising problems: challenges and future research opportunities. Array (2022). https://​doi.​org/​10.​1016/​j.​array.​2022.​100265View Article
  13. M. Ben Abdallah, J. Malek, A.A. Taher, H. Belmabrouk, J.E. Monreal, K. Krissian, Adaptive noise-reducing anisotropic diffusion filter. Nat. Comput. Appl. 27(5), 1273–1300 (2015)
  14. M. BenAbdallah, A.A. Taher, H. Guedri, J. Malek, H. Belmabrouk, Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation. Neural Comput. Appl. 29(8), 159–180 (2018). https://​doi.​org/​10.​1007/​s00521-016-2811-9View Article
  15. K. Bnou, S. Raghay, A. Hakim, A wavelet denoising approach based on unsupervised learning model. EURASIP J. Adv. Signal Process. (2020). https://​doi.​org/​10.​1186/​s13634-020-00693-View Article
  16. R.A. Boie, I.J. Cox, An analysis of camera noise. IEEE Trans. Pattern Anal. Mach. Intell. 145(6), 671–674 (1992). https://​doi.​org/​10.​1109/​34.​141557View Article
  17. P. Bouboulis, K. Slavakis, S. Theodoridis, Adaptive kernel-based image denoising employing semi-parametric regularization. IEEE Trans. Image Process. 19(6), 1465–1479 (2010). https://​doi.​org/​10.​1109/​TIP.​2010.​2042995MathSciNetView Article
  18. A. Buades, Non-local algorithm for image denoising. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 60–65 (2005)
  19. A. Buades, B. Coll, J.-M. Morel, A review of image denoising algorithms, with a new one. SIAM Interdiscip. J. 4, 490–530 (2005)MathSciNet
  20. S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000). https://​doi.​org/​10.​1109/​83.​862633MathSciNetView Article
  21. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process. 18, 1438–1451 (2009)MathSciNetView Article
  22. H. Choi, J. Jeong, Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sens. 11, 1184 (2019). https://​doi.​org/​10.​3390/​rs11101184View Article
  23. H. Chun, K. Guo, H. Chen, An improved image filtering algorithm for mixed noise. Appl. Sci. 11(21), 10358 (2021). https://​doi.​org/​10.​3390/​app112110358View Article
  24. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising with block-matching and 3D filtering. Proc. SPIE (2006). https://​doi.​org/​10.​1117/​12.​643267View Article
  25. T. Dai, Y. Zhang, L. Dong, L. Li, X. Liu, S. Xia, Content-aware bilateral filtering, in IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi'an, China, 1–6 (2018). https://​doi.​org/​10.​1109/​BigMM.​2018.​8499063.
  26. J. Delon, A. Houdard, Gaussian priors for image denoising of photographic images and video: fundamentals, open challenges and new trends, 319-96029-6, 978-3-319-96029-6 (2018).
  27. K.T. Dilna, D.J. Hemanth, Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus. Open Comput. Sci. 2021(11), 399–410 (2021)View Article
  28. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736 (2006)MathSciNetView Article
  29. A. El Gamal, H. Eltoukhy, CMOS image sensors. IEEE Circuits Devices Mag. 21(3), 6–20 (2005)View Article
  30. L. Fan, F. Zhang, H. Fan, C. Zhang, Brief review of image denoising techniques. Vis. Comput. Ind. Biomed. Art 2, 7 (2019). https://​doi.​org/​10.​1186/​s42492-019-0016-7View Article
  31. H. Faraji, W.J. Maclean, CCD noise removal in digital images. IEEE Trans. Image Process. (2006). https://​doi.​org/​10.​1109/​TIP.​2006.​877363.​2676-2685View Article
  32. X. Feng, Z. Pan, Detail enhancement for infrared images based on relativity of gaussian-adaptive bilateral filter. OSA Continuum 4(10), 2671–2686 (2021). https://​doi.​org/​10.​1364/​OSAC.​434858View Article
  33. A. Foi, M. Trimeche, V. Katkovnik, K. Egiazarian, Senior member, IEEE, (2007). Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Transactions, TIP-03364-2007-FINAL, 1–18 (2007).
  34. J. Fridrich, Digital image forensics. IEEE Signal Process. Mag. 26(2), 26–37 (2009). https://​doi.​org/​10.​1109/​MSP.​2008.​931078
  35. V.S. Frost, J.A. Stiles, K.S. Shanmugan, J.C. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982). https://​doi.​org/​10.​1109/​tpami.​1982.​4767223View Article
  36. M. Gao, B. Kang, X. Feng, W. Zhang, W. Zhang, Anisotropic diffusion based multiplicative speckle noise removal. Sensors 19(14), 3164 (2019). https://​doi.​org/​10.​3390/​s19143164View Article
  37. M. Gatcha, F. Messelmi, S. Saadi, An anisotropic diffusion adaptive filter for image denoising and restoration applied on satellite remote sensing images: a case study. Eng. Technol. Appl. Sci. Res. 12(6), 9715–9719 (2022). https://​doi.​org/​10.​48084/​etasr.​5363View Article
  38. M. Gharbi, C. Gaurav, S. Paris, F. Durand, Deep joint demosaicking and denoising. ACM Trans. Graph. 35, 1–12 (2016)View Article
  39. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (PrenticeHall Inc, Upper Saddle River, 2006)
  40. N. Guizard, K. Nakamura, V.S. Fonov, D.L. Arnold, D.L. Collins, Non-local means inpainting of MS lesions in longitudinal image processing. Front. Neurosci. 9, 456 (2015)View Article
  41. B. Guo, K. Song, H. Dong, Y. Yan, Z. Tu, L. Zhu, NERNet: Noise estimation and removal network for image denoising. J. Vis. Commun. Image Represent. 71, 1047–3203 (2020). https://​doi.​org/​10.​1016/​j.​jvcir.​2020.​102851View Article
  42. R.M. Haralick, L.G. Shapiro, Image segmentation techniques. Comput. Vis. Graph. Image Process. 29, 100–132 (1985). https://​doi.​org/​10.​1016/​S0734-189X(85)90153-7View Article
  43. G.E. Healey, R. Kondepudy, Radiometric CCD camera calibration and noise estimation. IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 267–276 (1994)View Article
  44. K. Huang, H. Zhu, Image noise removal method based on improved nonlocal mean algorithm. Complexity, Hindawi (2021). https://​doi.​org/​10.​1155/​2021/​5578788
  45. C. Hyunho, J. Jechang, Speckle noise reduction technique for SAR images using SRAD and gradient domain guided image filtering, in International Workshop on Advanced Imaging Technology (IWAIT) 2020. https://​doi.​org/​10.​1117/​12.​2566244 (2020).
  46. K. Irie, A.E. McKinnon, K. Unsworth, I.M. Woodhead, A technique for evaluation of CCD video-camera noise. IEEE Trans. Circuits Syst. Video Technol. 18(2), 280–284 (2008). https://​doi.​org/​10.​1109/​TCSVT.​2007.​913972View Article
  47. Y. Jiang, H. Wang, Y. Cai, B. Fu, Salt and pepper noise removal method based on the edge-adaptive total variation model. Front. Appl. Math. Stat. (2022). https://​doi.​org/​10.​3389/​fams.​2022.​918357View Article
  48. Q. Jin, I. Grama, C. Kervrann, Q. Liu, Nonlocal means and optimal weights for noise removal. SIAM J. Imag. Sci. 10(4), 1878–1920 (2017)MathSciNetView Article
  49. P.L. Joseph Raj, K. Kalimuthu, S. Gauni, C.T. Manimegalai, Extended speckle reduction anisotropic diffusion filter to despeckle ultrasound images. Intell. Autom. Soft Comput. 34(2), 1187–1196 (2022). https://​doi.​org/​10.​32604/​iasc.​2022.​026052View Article
  50. N. Joshi and S. Jain, An improved anisotropic diffusion filtering approach for noise reduction in MRI, in 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1–5 (2021). https://​doi.​org/​10.​1109/​ICRITO51393.​2021.​9596244.
  51. K. Krissian, C.F. Westin, R. Kikinis, K.G. Vosburgh, Oriented speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 16(5), 1412–1424 (2007). https://​doi.​org/​10.​1109/​tip.​2007.​891803MathSciNetView Article
  52. D. Kuan, A. Sawchuk, T. Strand, P. Chavel, Adaptive restoration of images with speckle. IEEE Trans. Acoust. Speech Signal Process. 35, 373–383 (1987)View Article
  53. N. Kumar, A.K. Dahiya, K. Kumar, Modified median filter for image denoising. Int. J. Adv. Sci. Technol. 29(4), 1495–1502 (2020)
  54. J.S. Lee, Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2(2), 165–168 (1980). https://​doi.​org/​10.​1109/​TPAMI.​1980.​4766994View Article
  55. J. Li, Y. Wu, Y. Zhang, J. Zhao, Y. Si, Parameter estimation of Poisson–Gaussian signal-dependent noise from single image of CMOS/CCD image sensor using local binary cyclic jumping. MDPI Sens. 21(24), 8330 (2021). https://​doi.​org/​10.​3390/​s21248330View Article
  56. Y. Li, Z. Li, K. Wei, W. Xiong, J. Yu, B. Qi, Noise estimation for image sensor based on local entropy and median absolute deviation. Sensors 19(2), 339 (2019). https://​doi.​org/​10.​3390/​s19020339View Article
  57. Y. Li, C. Liu, X. You, J. Liu, A single-image noise estimation algorithm based on pixel-level low-rank low-texture patch and principal component analysis. Sensors 22, 8899 (2022). https://​doi.​org/​10.​3390/​s22228899View Article
  58. Y. Li, et al., NTIRE 2023 challenge on image denoising: methods and results, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 1904–1920 (2023).
  59. C. Liu, R. Szeliski, S. Bing Kang, C. Lawrence Zitnick, W.T. Freeman, Automatic estimation and removal of noise from a single. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 299–314 (2008)View Article
  60. C. Liu, W.T. Freeman, R. Szeliski, S.B. Kang, Noise estimation from a single image, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, USA. 207, p. 901–908 (2006). https://​doi.​org/​10.​1109/​CVPR.
  61. S. Liu et al., SAR speckle removal using hybrid frequency modulations. IEEE Trans. Geosci. Remote Sens. 59(5), 3956–3966 (2021). https://​doi.​org/​10.​1109/​TGRS.​2020.​3014130View Article
  62. X. Liu, T. Masayuki, M. Okutomi, Estimation of signal dependent noise parameters from a single image. IEEE Int. Conf. Image Process. (2013). https://​doi.​org/​10.​1109/​ICIP.​2013.​6738017View Article
  63. X. Liu, M. Tanaka, M. Okutomi, Single-image noise level estimation for blind denoising. IEEE Trans. Image Process. 22(12), 5226–5237 (2013). https://​doi.​org/​10.​1109/​TIP.​2013.​2283400View Article
  64. J. Lukáš, J. Fridrich, M. Goljan, Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2005). https://​doi.​org/​10.​1109/​TIFS.​2006.​873602View Article
  65. O. Magud, E. Tuba, N. Bacanin, Medical ultrasound image speckle noise reduction by adaptive median filter. Wseas Trans. Biol. Biomed. 14, 38–46 (2017)
  66. B. Maiseli, Nonlinear anisotropic diffusion methods for image denoising problems: challenges and future research opportunities. Array (2023). https://​doi.​org/​10.​1016/​j.​array.​2022.​100265View Article
  67. S. Mohammadnejad, S. Roshani, M.N. Sarvi, Fixed pattern noise reduction method in CCD sensors for LEO satellite applications, in 11th International Conference on Telecommunications—ConTEL 2011, p. 15–17 (2011).
  68. J. Nakamura, Image Sensors and Signal Processing for Digital Still Cameras (CRC Press, Boca Raton, 2006)
  69. R.R. Nair, E. David, R. Sivakumar, A robust anisotropic diffusion filter with ow arithmetic complexity for images. EURASIP J. Image Video Process. 48, 2–14 (2019). https://​doi.​org/​10.​1186/​s13640-019-0444-5View Article
  70. M. Niemeijer, J. Staal, B.V. Ginneken, M. Loog, M.D. Abràmoff, Comparative study of retinal vessel segmentation methods on a new publicly available database. in J.M. Fitzpatrick, M. Sonka. Retrieved from https://​drive.​grand-challenge.​org/​ (2004).
  71. J. Nyunt, Y. Sugiura, T. Shimamura, Noise level estimation on weak-texture image patch with image power spectrum sparsity. J. Signal Process. 23(3), 95–103 (2019). https://​doi.​org/​10.​2299/​jsp.​23.​95View Article
  72. S. Panchacharam, M. Giriprasad, An image enhancement approach to achieve high speed using adaptive modified bilateral filter for satellite images using FPGA. TELKOMNIKA Telecommun. Comput. Electron. Control 15(4), 1766–1775 (2017). https://​doi.​org/​10.​12928/​TELKOMNIKA.​v15i4.​3457View Article
  73. S. Paris, P. Kornprobst, J. Tumblin, F. Durand, Bilateral filtering: theory and applications. Found. Trends Comput. Graph. Vis. 4(1), 1–73 (2008). https://​doi.​org/​10.​1561/​0600000020View Article
  74. P. Perona, J. Malik, Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)View Article
  75. W.K. Pratt, Generalized Wiener filtering computation techniques. IEEE Trans. Comput. 21(7), 636–641 (1972). https://​doi.​org/​10.​1109/​T-C.​1972.​223567View Article
  76. S. Pyatykh, J. Hesser, L. Zheng, Image noise level estimation by principal component analysis. IEEE Trans. Image Process. 22(2), 687–699 (2013). https://​doi.​org/​10.​1109/​TIP.​2012.​2221728MathSciNetView Article
  77. M. Raitoharju, H. Nurminen, D. Cilden-Guler, S. Särkkä, Kalman filtering with empirical noise models. Applications (2021). https://​doi.​org/​10.​48550/​arXiv.​2105.​08514
  78. D. Sage, M. Unser, Teaching image-processing programming in Java. IEEE Signal Process. Mag. 20(6), 43–52 (2003)View Article
  79. U. Sakoglu, R.C. Hardie, M.M. Hayat, B.M. Ratlif, J. Scott Tyo, An algebraic restoration method for estimating fixed-pattern noise in infrared imagery from a video sequence, in Applications of Digital Image Processing XXVII, 5558, 69–79 (2004).
  80. L. Shuaiqi, Q. Hu, P. Li, J. Zhao, M. Liu, Z. Zhu, Speckle suppression based on weighted nuclear norm minimization and grey theory. IEEE Trans. Geosci. Remote Sens. 57, 2700–2708 (2019)View Article
  81. H. Si, Z. Wei, Z. Zhu, H. Chen, D. Liang, W. Wang, M. Wei. LBF: learnable bilateral filter for point cloud denoising, in Computer Vision and Pattern Recognition (cs.CV) (2022).
  82. H. Singh, S.V.R. Kommuri, A. Kumar, V. Bajaj, A new technique for guided filter based image denoising using modified cuckoo search optimization. Expert Syst. Appl. (2021). https://​doi.​org/​10.​1016/​j.​eswa.​2021.​114884View Article
  83. O. Skorka, D. Joseph, Design and fabrication of vertically-integrated CMOS image sensors. Sensors (2011). https://​doi.​org/​10.​3390/​s110504512View Article
  84. V. Stojanovic, N.N. Nedic, Joint state and parameter robust estimation of stochastic nonlinear systems. Int. J. Robust Nonlinear Control (2015). https://​doi.​org/​10.​1002/​rnc.​3490View Article
  85. V. Stojanovic, N.N. Nedic, Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non-Gaussian noise. Int. J. Robust Nonlinear Control (2015). https://​doi.​org/​10.​1002/​rnc.​3319View Article
  86. V. Stojanovic, V. Filipovic, Adaptive input design for identification of output error model with constrained. Circuits Syst. Signal Process. 33, 97–113 (2014). https://​doi.​org/​10.​1007/​s00034-013-9633-0MathSciNetView Article
  87. Y. Sun, L. Lei, D. Guan, X. Li, G. Kuang, SAR image speckle reduction based on nonconvex hybrid total variation model. IEEE Trans. Geosci. Remote Sens. 59(2), 1231–1249, (2020). https://​doi.​org/​10.​1109/​TGRS.​2020.​3002561View Article
  88. A. Suneetha, E.S. Reddy, Robust Gaussian noise detection and removal in color images using modified fuzzy set filter. J. Intell. Syst. (2020). https://​doi.​org/​10.​1515/​jisys-2019-0211View Article
  89. M. Tanaka, Noise Level Estimation from a Single Image (https://​www.​mathworks.​com/​matlabcentral/​fileexchange/​36921-noise-level-estimation-from-asingle-image), MATLAB Central File Exchange. Retrieved September 22, (2023).
  90. K.V. Thakur, O.H. Damodare, A.M. Sapkal, Poisson noise reducing bilateral filter, in 7th International Conference on Communication, Computing and Virtualization, 2016, p. 861–865 (2016). https://​doi.​org/​10.​1016/​j.​procs.​2016.​03.​087.
  91. N. Thakur, N.U. Khan, S.D. Sharma, A review on performance analysis of PDE based anisotropic approaches for image enhancement. Informatica (2020). https://​doi.​org/​10.​31449/​inf.​v45i6.​3333View Article
  92. T.H. Thai, F. Retraint, R. Cogranne, Generalized signal-dependent noise model and parameter estimation for natural images. Signal Process. (2015). https://​doi.​org/​10.​1016/​j.​sigpro.​2015.​02.​020View Article
  93. S. Thayammal, G. Sankaramalliga, S. Priyadarsini, K. Ramalakshmi, Performance analysis of image denoising using deep convolutional neural network. IOP Conf. Ser. Mater. Sci. Eng. (2021). https://​doi.​org/​10.​1088/​1757-899X/​1070/​1/​012085View Article
  94. C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, C.-W. Lin, Deep learning on image denoising: an overview. Neural Netw. (2020). https://​doi.​org/​10.​1016/​j.​neunet.​2020.​07.​025,131,251-275View Article
  95. H. Tian, B. Fowler, A. El Gamal, Analysis of temporal noise in CMOS photodiode active pixel sensor. IEEE J. Solid-State Circuits 36(1), 92–101 (2001)View Article
  96. M. Tiwari, B. Gupta, Image denoising using spatial gradient based bilateral filter and minimum mean square error filtering. Procedia Comput. Sci. 54, 638–645 (2015). https://​doi.​org/​10.​1016/​j.​procs.​2015.​06.​074View Article
  97. C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India, p. 839–846 (1998). https://​doi.​org/​10.​1109/​ICCV.​1998.​710815.
  98. G. Torricelli, F. Argenti, L. Alparone, Modelling and assessment of signal-dependent noise for image de-noising, in 11th European Signal Processing Conference (2002). https://​doi.​org/​10.​5281/​ZENODO.​38005 (2002).
  99. L.A. Tran, Image Processing Course Project: Image Filtering with Wiener Filter and Median Filter (2019).https://​doi.​org/​10.​13140/​RG.​2.​2.​15700.​65921
  100. Y. Tsin, V. Ramesh, T. Kanade, Statistical calibration of CCD imaging process, in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 1, p. 480–487 (2001).
  101. N. Upadhyay, R.K. Jaiswal, Single channel speech enhancement: using wiener filtering with recursive noise estimation. Procedia Comput. Sci. 84, 22–30 (2016). https://​doi.​org/​10.​1016/​j.​procs.​2016.​04.​061View Article
  102. F. Wagner, M. Thies, M. Gu, Y. Huang, S. Pechmann, M. Patwari, S. Ploner, O. Aust, S. Uderhardt, G. Schett, S. Christiansen, A. Maier, Ultralow-parameter denoising: trainable bilateral filter layers in computed tomography. Med. Phys. 47, 5107–5120 (2022). https://​doi.​org/​10.​1002/​mp.​15718View Article
  103. J. Xiao, H. Tian, Y. Zhang, Y. Zhou, J. Lei, Blind video denoising via texture-aware noise estimation. Comput. Vis. Image Underst. 169, 1–13 (2018). https://​doi.​org/​10.​1016/​j.​cviu.​2017.​11.​012View Article
  104. S. Xu, X. Zeng, Y. Jiang, Y. Tang, A multiple image-based noise level estimation algorithm. IEEE Signal Process. Lett. 24(11), 1701–1705 (2017). https://​doi.​org/​10.​1109/​LSP.​2017.​2755687View Article
  105. T. Yang, B. Xu, B. Zhou, W. Wei, A nonlinear diffusion model with smoothed background estimation to enhance degraded images for defect detection. Appl. Sci. 13, 211 (2023). https://​doi.​org/​10.​3390/​app13010211View Article
  106. H. Yao, M. Zou, C. Qin, X. Zhang, Signal-dependent noise estimation for a real-camera model via weight and shape constraints. IEEE Trans. Multimed. 24, 640–654 (2022). https://​doi.​org/​10.​1109/​TMM.​2021.​3056879View Article
  107. L. Yu. Image noise preprocessing of interactive projection system based on switching filtering scheme. Hindawi, 1076–2787 (2018). https://​doi.​org/​10.​1155/​2018/​1258306
  108. Y. Yu, S.T. Acton, Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002). https://​doi.​org/​10.​1109/​TIP.​2002.​804276MathSciNetView Article
  109. Y. Yu, S.T. Acton, Edge detection in ultrasound imagery using the instantaneous coefficient of variation. IEEE Trans. Image Proccess. 13(12), 1640–1655 (2004)View Article
  110. M. Zhang, B.K. Gunturk, Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008). https://​doi.​org/​10.​1109/​TIP.​2008.​2006658MathSciNetView Article
  111. G. Zhang, F. Guo, Q. Zhang, K. Xu, P. Jia, X. Hao, Speckle reduction by directional coherent anisotropic diffusion. Remote Sens. 11(23), 2768 (2019). https://​doi.​org/​10.​3390/​rs11232768View Article
  112. K. Zhang, W. Zuo, L. Zhang, FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018). https://​doi.​org/​10.​1109/​TIP.​2018.​2839891MathSciNetView Article
  113. Q. Zhao, C. Debao, Improved weighted nonlocal mean algorithm filter for image. J. Electron. Meas. Instrum. 28(3), 334–339 (2014)
  114. Y. Zhu, C. Huang, An improved median filtering algorithm for image noise reduction, in 2012 International Conference on Solid State Devices and Materials Science. Elsevier, 25, 609–616 (2012).
  115. M. Zou, H. Yao, C. Qin, X. Zhang, Statistical analysis of signal-dependent noise: application in blind localization of image splicing forgery. Comput. Sci. Comput. Vis. Pattern Recognit. (2020).
Metadaten
Titel
A Modified Anisotropic Diffusion Scheme for Signal-Dependent Noise Filtering
verfasst von
Mariem Ben Abdallah
Jihene Malek
Abdullah Bajahzar
Hafedh Belmabrouk
Publikationsdatum
11.12.2023
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 4/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-023-02538-5