COMPARATIVE ANALYSIS OF TWO FILTERING METHODS TO ELIMINATE NOISE IN AN IMAGE OF DIFFERENT DEGREES OF NOISE

  • K.O. Sever Southern Federal University
  • I.I. Turulin Southern Federal University
  • D.A. Guzhva Southern Federal University
Keywords: Image processing, impulse noise, Gaussian noise, filters, median filter, Wiener filter

Abstract

In modern photography and video technology, any image in the process of its creation is
distorted by various types of noise. There are various types of noise, but in practice, impulsive and
Gaussian noise models are the most common. Attenuation of the effect of noise is achieved by filtering.
At the moment, there is no universal filter that suppresses noise data at various intens ities
of distortion. Therefore, an important aspect is to determine the field of application of each
type of filter when suppressing noise in the image and creating a filter, consisting of a combination
of different filtering methods for optimal image cleaning. The article presents a comparative
analysis of median filtering and Wiener filtering to eliminate impulse and Gaussian noise in
the image with different degrees of noise. For modeling, we used one image, separately distorted
by impulse and separately by Gaussian noise with pixel distortion probabilities from 1% to
99% inclusive. Filtration was performed with windows equal to 3x3 and 5x5. As a result, we
obtained numerical estimates of the image filtering quality based on the peak signal-to-noise
ratio (PSNR). On the basis of the data obtained, the application of the investigated filters, their
modifications, advantages and disadvantages were analyzed, as well as recommendations for
their use were given. As a result of a comparative analysis of the studied types of filtering for
noisy images, it was found that the median filter with a 3x3 window copes better with image
cleaning from low-intensity impulse noise and with a 5x5 window - with image cleaning with an
average noise intensity. Also, the median filter does a better job of filtering out Waussian noise
at its medium and high rms deviations. The Wiener filter with 3x3 and 5x5 windows better fi lters
Gaussian noise at small values of its root-mean-square deviation. Also, the Wiener filter
copes better with impulse noise with relatively high noise power.

References

1. Gonzalez R.C., Woods R.E. Digital image processing, Upper Saddle River: Pearson Prentice
Hall, 2007, 976 p. ISBN: 978-0- 13-168728-8.
2. Gonsales R., Vuds R. Tsifrovaya obrabotka izobrazheniy [Digital image processing]. Moscow:
Tekhnosfera, 2005, 1072 p.
3. Vatolin D., Ratushnyak A., Smirnov M., Yukin V.V. Metody szhatiya dannykh. Ustroystvo
arkhivatorov, szhatie izobrazheniy i video [Data compression methods. The device of
archivers, image and video compression]. Moscow: DIALOG-MIFI, 2003, 384 p.
4. Gonzalez R.C., Woods R.E., Eddins S.L. Digital image processing using MATLAB, Upper
Saddle River, NJ: PrenticeHall, Inc., 2010, 344 p. ISBN: 978-0-9820854-0-0.
5. Problema podavleniya shuma na izobrazheniyakh i video i razlichnye podkhody k ee resheniyu
[The problem of noise suppression in images and videos and various approaches to its solution].
Available at: https://docplayer.ru/57372769-Problema-podavleniya-shuma-naizobrazheniyah-
i-video-i-razlichnye-podhody-k-ee-resheniyu.html (accessed 10 May 2021).
6. Buades A., Coll B. and Morel J.M. A review of image denoising algorithms, with a new one,
SIAM Multiscale Modeling and Simulation, 2005, Vol. 4, pp. 490-530.
7. Slozhenie i izmerenie shuma [Adding and measuring noise]. Available at:
https://support.ptc.com/help/mathcad/ru/index.html#page/PTC_Mathcad_Help%2Faddition_a
nd_noise_measurement.html%23 (accessed 11 May 2021).
8. Turulin I.I. Upravlyaemye tsifrovye fil'try: monografiya [Controlled digital filters: a monograph].
Taganrog: Izd-vo YuFU, 2016, 308 p.
9. Turulin I.I. Osnovy teorii rekursivnykh KIKH-fil'trov: monografiya [Fundamentals of the theory
of recursive FIR filters: monograph]. Taganrog: Izd-vo YuFU, 2016, 264 p.
10. Turulin I.I., Tkachenko M.G. Bystroperestraivaemye tsifrovye fil'try: monografiya [Fasttunable
digital filters: a monograph]. Taganrog: Izd-vo TTI YuFU, 2008, 104 p.
11. Starovoytov V.V., Golub Yu.I. Tsifrovye izobrazheniya: ot polucheniya do obrabotki [Digital
images: from receiving to processing]. Minsk: OIPI NAN Belarusi, 2014, 202 p. ISBN 978-
985-6744-80-1.
12. Rodionov S.A., Voznesenskiy N.B., Shchekol'yan E.M. Obrabotka rezul'tatov izmereniya
distorsii proektsionnykh ob"ektivov [Processing of the results of measuring the distortion of
projection lenses], Izvestiya vuzov. Priborostroenie [Izvestiya vuzov. Instrumentation], 1991,
Vol. XXXIV, No. 7, pp. 61-68.
13. Milenin N.K. Shumy v formirovatelyakh signala na PZS [Noise in signal formers on a CCD],
Tekhnika kino i televideniya [Film and television technology], 1980, No. 6, pp. 51-57.
14. Selyankin V.V., Skorokhod S.V. Analiz i obrabotka izobrazheniy v zadachakh komp'yuternogo
zreniya: ucheb. posobie [Image analysis and processing in computer vision problems: a textbook].
Taganrog: Izd-vo YuFU, 2015, 82 p.
15. Primer: Mediannoe i kvantil'noe fil'trovanie [Example: Median and quantile filtering]. Available
at: https://support.ptc.com/help/mathcad/ru/index.html#page/PTC_Mathcad_Help%
2Fexample_ median_and_quantile_filtering.html%23 (accessed 12 May 2021).
16. Fabijańska A., Sankowski D. Noise adaptive switching median-based filter for impulse noise
removal from extremely corrupted images, IET Image Processing, 2011, Vol. 5, Issue 5,
pp. 472-480. – Doi: 10.1049/iet-ipr.2009.0178.
17. Ng P.-E., Ma K.-K. A switching median filter with boundary discriminative noise detection for
extremely corrupted images, IEEE Transactions on Image Processing, 2006, Vol. 15, Issue 6,
pp. 1506-1516. Doi: 10.1109/TIP.2005.871129.
18. Peixuan Z., Fang L. A new adaptive weighted mean filter for removing salt-and-pepper noise,
IEEE Signal Processing Letters, 2014, Vol. 21, Issue 10, pp. 1280-1283. Doi:
10.1109/LSP.2014.2333012.
19. Roy A,. Singha J., Manam L., Laskar R.H. Combination of adaptive vector median filter and
weighted mean filter for removal of high-density impulse noise from colour images, IET Image
Processing, 2017, Vol. 11, Issue 6, pp. 352-361. Doi: 10.1049/iet-ipr.2016.0320.
20. Hsieh M.H., Cheng F.H., Shie M.C., Ruan S.J. Fast and efficient median filter for removing
21. 1–99% levels of salt-and-pepper noise in images, Engineering Applications of Artificial Intelligence,
2013, Vol. 26 (4), pp. 1333-1338. Doi: 10.1016/j.engappai.2012.10.012.
22. Gruzman I.S., Kirichuk V.S., Kosykh V.P., Peretyagin G.I., Spektor A.A. Tsifrovaya obrabotka
izobrazheniy v informatsionnykh sistemakh: ucheb. posobie [Digital image processing in information
systems: a textbook]. Novosibisrk: Izd-vo NGTU, 2002, 352 p.
Published
2021-08-11
Section
SECTION I. INFORMATION PROCESSING ALGORITHMS