Skip to main content

2020 | OriginalPaper | Buchkapitel

Image Denoising Using Spatial and Frequency Domain Filters: A Tool for Image Quality Enhancement

verfasst von : Santhi Krishnamoorthi, Nirmala Madian, Dhanasekaran Rajagopal

Erschienen in: Innovations in Electrical and Electronics Engineering

Verlag: Springer Singapore

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

search-config
loading …

Abstract

Image denoising plays a vital role in image analysis. Image denoising helps in removing noise in the image by which the quality of the image is improved. Image denoising is mainly used in photographic images for enhancing the quality, medical image analysis where inbuilt noises are present due to images obtained from various imaging modalities. These noises should be removed to get a better understanding of medical images to detect tumor, cancer, and other diseases. Denoising helps in improving the image fidelity in satellite images. This work focuses on performing image denoising by various spatial and frequency domain filters on photographic images to enhance the image quality. The comparative analysis of all these filters is analyzed based on quality metrics like peak signal-to-noise ratio (PSN)R and mean square error (MSE).

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!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat J.C. Church, Y. Chen, S.V. Rice, A spatial median filter for noise removal in digital images, in IEEE, pp. 618–623 (2008) J.C. Church, Y. Chen, S.V. Rice, A spatial median filter for noise removal in digital images, in IEEE, pp. 618–623 (2008)
2.
Zurück zum Zitat R.H. Chan, C.-W. Ho, M. Nikolova, Salt and pepper noise removal by median-type noise detectors and detail preserving regularization. IEEE Trans. Image Process. 14(10), 1479–1485 (2005)CrossRef R.H. Chan, C.-W. Ho, M. Nikolova, Salt and pepper noise removal by median-type noise detectors and detail preserving regularization. IEEE Trans. Image Process. 14(10), 1479–1485 (2005)CrossRef
3.
Zurück zum Zitat S. Kumar, P. Kumar, M. Gupta, A.K. Nagawat, Performance comparison of median and wiener filter in image de-noising. Int. J. Comput. Appl. 12(4), 27–31 (2010) S. Kumar, P. Kumar, M. Gupta, A.K. Nagawat, Performance comparison of median and wiener filter in image de-noising. Int. J. Comput. Appl. 12(4), 27–31 (2010)
4.
Zurück zum Zitat T. Huang, G. Yang, G. Tang, A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust. Speech Sign. Process. 27(1), 13–18 (1979)CrossRef T. Huang, G. Yang, G. Tang, A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust. Speech Sign. Process. 27(1), 13–18 (1979)CrossRef
5.
Zurück zum Zitat H. Ibrahim, N.S. Kong, T.F. Ng, Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Consumer Electron. 54(4), 1920–1927 (2008)CrossRef H. Ibrahim, N.S. Kong, T.F. Ng, Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Consumer Electron. 54(4), 1920–1927 (2008)CrossRef
6.
Zurück zum Zitat S. Akkoul, R. Ledee, R. Leconge, R. Harba, A new adaptive switching median filter. IEEE Signal Process. Lett. 17(6), 587–590 (2010)CrossRef S. Akkoul, R. Ledee, R. Leconge, R. Harba, A new adaptive switching median filter. IEEE Signal Process. Lett. 17(6), 587–590 (2010)CrossRef
7.
Zurück zum Zitat A. Fabiaska, D. Sankowski, Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. IET Image Process. 5(5), 472–480 (2011)CrossRef A. Fabiaska, D. Sankowski, Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. IET Image Process. 5(5), 472–480 (2011)CrossRef
8.
Zurück zum Zitat S.D. Ruikar, D. Doye, Wavelet based image denoising technique. Int. J. Adv. Comput. Sci. Appl. 2(3) (2011) S.D. Ruikar, D. Doye, Wavelet based image denoising technique. Int. J. Adv. Comput. Sci. Appl. 2(3) (2011)
9.
Zurück zum Zitat S.A. Murugan, K. Karthikeyan, N.A. Natraj, C.R. Rathish, Speckle noise removal using dual tree complex wavelet transform. Int. J. Sci. Technol. Res. 2(8) (2013) (ISSN: 2277-8616) S.A. Murugan, K. Karthikeyan, N.A. Natraj, C.R. Rathish, Speckle noise removal using dual tree complex wavelet transform. Int. J. Sci. Technol. Res. 2(8) (2013) (ISSN: 2277-8616)
10.
Zurück zum Zitat P. Perona, J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRef P. Perona, J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRef
11.
Zurück zum Zitat J. Weickert, A review of nonlinear diffusion filtering, in Scale-Space Theory in Computer Vision (Springer, LNCS, 1997), vol. 1252, pp. 1–28 J. Weickert, A review of nonlinear diffusion filtering, in Scale-Space Theory in Computer Vision (Springer, LNCS, 1997), vol. 1252, pp. 1–28
12.
Zurück zum Zitat M. Kazubek, Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Sign. Proces. lett. 10(11), 324–326 (2003)CrossRef M. Kazubek, Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Sign. Proces. lett. 10(11), 324–326 (2003)CrossRef
13.
Zurück zum Zitat A. Buades, B. Coll, J Morel, A non-local algorithm for image denoising, in IEEE International Conference on Computer Vision and Pattern Recognition (2005) A. Buades, B. Coll, J Morel, A non-local algorithm for image denoising, in IEEE International Conference on Computer Vision and Pattern Recognition (2005)
14.
Zurück zum Zitat G. Andria, F. Attivissimo, G. Cavone, A.M.L. Lanzolla, Selection of wavelet functions and thresholding parameters in ultrasound image denoising, in Medical Measurements and Applications Proceedings, IEEE International Conference, pp. 49–52 (2013) G. Andria, F. Attivissimo, G. Cavone, A.M.L. Lanzolla, Selection of wavelet functions and thresholding parameters in ultrasound image denoising, in Medical Measurements and Applications Proceedings, IEEE International Conference, pp. 49–52 (2013)
15.
Zurück zum Zitat S. Khera, S. Malhotra, Survey on medical image de noising using various filters and wavelet transform. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(4), 230–234 (2014) S. Khera, S. Malhotra, Survey on medical image de noising using various filters and wavelet transform. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(4), 230–234 (2014)
16.
Zurück zum Zitat A.K. Bains, P. Sandhu, Image denoising using curvelet transform. Int. J. Curr. Eng. Technol. 5(1), 490–493 (2015) A.K. Bains, P. Sandhu, Image denoising using curvelet transform. Int. J. Curr. Eng. Technol. 5(1), 490–493 (2015)
17.
Zurück zum Zitat M.M. Bobby, Performance comparison between filters and wavelet transform in image de noising for different noises. Int. J. Comput. Sci. Commun. 2(2), 637–639 (2011) M.M. Bobby, Performance comparison between filters and wavelet transform in image de noising for different noises. Int. J. Comput. Sci. Commun. 2(2), 637–639 (2011)
18.
Zurück zum Zitat C. Boncelet, Image noise models, in ed. by A.C. Bovik. Handbook of Image and Video Processing (2005) C. Boncelet, Image noise models, in ed. by A.C. Bovik. Handbook of Image and Video Processing (2005)
19.
Zurück zum Zitat R. Srinivas, S. Panda, Performance analysis of various filters for image noise removal in different noise environment. Int. J. Adv. Comput. Res. 3(13), 47–52 (2013). (ISSN (online): 2277-7970) R. Srinivas, S. Panda, Performance analysis of various filters for image noise removal in different noise environment. Int. J. Adv. Comput. Res. 3(13), 47–52 (2013). (ISSN (online): 2277-7970)
20.
Zurück zum Zitat P. Agrawal, J.S. Verma, A survey of linear and non-linear filters for noise reduction. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1(3) (2013) P. Agrawal, J.S. Verma, A survey of linear and non-linear filters for noise reduction. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1(3) (2013)
21.
Zurück zum Zitat N.P. Bhosale1, R. Manza, K.V. Kale, Analysis of effect of Gaussian, salt and pepper noise removal from noisy remote sensing images, in Proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications, Elsevier, pp. 386–390 (2014) N.P. Bhosale1, R. Manza, K.V. Kale, Analysis of effect of Gaussian, salt and pepper noise removal from noisy remote sensing images, in Proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications, Elsevier, pp. 386–390 (2014)
23.
Zurück zum Zitat R. Biswas, D. Purkayastha, S. Roy in Denoising of MRI Images Using Curvelet Transform, eds. by A. Konkani, R. Bera, S. Paul. Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol. 442 (Springer, Singapore, 2018) R. Biswas, D. Purkayastha, S. Roy in Denoising of MRI Images Using Curvelet Transform, eds. by A. Konkani, R. Bera, S. Paul. Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol. 442 (Springer, Singapore, 2018)
24.
Zurück zum Zitat S. Gupta, S. Roy, Medav filter—filter for removal of image noise with the combination of median and average filters, in Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, vol. 727, eds. by S. Bhattacharyya, A. Mukherjee, H. Bhaumik, S. Das, K. Yoshida (Springer, Singapore, 2019) S. Gupta, S. Roy, Medav filter—filter for removal of image noise with the combination of median and average filters, in Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, vol. 727, eds. by S. Bhattacharyya, A. Mukherjee, H. Bhaumik, S. Das, K. Yoshida (Springer, Singapore, 2019)
Metadaten
Titel
Image Denoising Using Spatial and Frequency Domain Filters: A Tool for Image Quality Enhancement
verfasst von
Santhi Krishnamoorthi
Nirmala Madian
Dhanasekaran Rajagopal
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-2256-7_70