A unified block-based sparse domain solution for quasi-periodic de-noising from different genres of images with iterative filtering

  • D. ChakrabortyEmail author
  • A. Chakraborty
  • A. Banerjee
  • S. R. Bhadra Chaudhuri


Images, corresponding to various crucial imagery applications often experience stern problem of being degraded by different modalities of periodic/quasi-periodic noises. Though few periodic denoising algorithms address well for some specific application only, most of them fail to focus on the problem as a whole. In this article, a unified solution is presented which performs well for most of the vital non-natural imagery applications having dissimilar modalities. Initially, we divide the corrupted image into several blocks and then average those to get an averaged spatial image block. This block gets convolved with the Kaiser-Window to avoid any unnecessary artifacts followed by the spectral domain transformation. Our proposed algorithm relies on steadily decreasing characteristic of any uncorrupted natural image’s power spectra to expect a model by grossly reducing induced noise. An image feature based adaptive threshold is then applied on error spectra to precisely perceive unexpectedly high spectral amplitudes as the outliers. It is then interpolated to the actual size of the corrupted image, containing noisy spectra on which a proposed recursively adaptive notch-reject filter is applied. Extensive and detailed study of performance comparison with other state-of-the-art algorithms proves the supremacy of our proposed strategy.


Periodic/quasi-periodic noise Adaptive natural image modelling Kaiser-Window Adaptive thresholding Butterworth smoothing profile Recursive notch-reject filter 



  1. 1.
  2. 2.
  3. 3.
    Aizenberg I, Butakoff C (2002) Non-linear frequency domain filter for quasi periodic noise removal. In: Proceeding of International TICSP Workshop on Spectra Methods and Multi-rate Signal Processing. TICSP Series, 17, pp. 147–153Google Scholar
  4. 4.
    Aizenberg I, Butakoff C (2002) Frequency domain median-like filter for periodic and quasi-periodic noise removal. In: SPIE Proceedings of the Image Processing, 4667, pp. 181–191Google Scholar
  5. 5.
    Aizenberg I, Butakoff C (2008) A windowed Gaussian notch filter for quasi-periodic noise removal. Image Vis Comput 26(10):1347–1353CrossRefGoogle Scholar
  6. 6.
    Al-Najjar YA, Soong DC (2012) Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. Int J Sci Eng Res 3(8), ISSN 2229-5518):1–5Google Scholar
  7. 7.
    Arce GR, Lau DL (2002) Method and apparatus for producing halftone images using green-noise masks having adjustable coarseness, U.S. Patent No. 6,493,112/ (US6493112 B1). U.S. Patent and Trademark Office, Washington, DCGoogle Scholar
  8. 8.
    Blieberger J, Lieger R (1996) Worst-case space and time complexity of recursive procedures. Real-Time Systems 11(2):115–144CrossRefGoogle Scholar
  9. 9.
    Chakraborty D et al (2016) A proficient method for periodic and quasi-periodic noise fading using spectral histogram thresholding with sinc restoration filter. International Journal of Electronics and Communications (AEÜ) 70(12):1580–1592CrossRefGoogle Scholar
  10. 10.
    Chang Y et al (2015) Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping. IEEE Trans Image Process 24(6):1852–1866MathSciNetCrossRefGoogle Scholar
  11. 11.
    Cornelis B et al (2012) Digital canvas removal in paintings. Signal Process 92(4):1166–1171CrossRefGoogle Scholar
  12. 12.
    Cornelis B et al (2017) Removal of canvas patterns in digital acquisitions of paintings. IEEE Trans Image Process 26(1):160–171MathSciNetCrossRefGoogle Scholar
  13. 13.
    Eaton P, West P (2010) Atomic force microscopy. Oxford University PressGoogle Scholar
  14. 14.
    Fehrenbach J, Weiss P, Lorenzo C (2012) Variational algorithms to remove stationary noise: applications to microscopy imaging. IEEE Trans Image Process 21(10):4420–4430MathSciNetCrossRefGoogle Scholar
  15. 15.
    Giglio L, Csiszar I, Justice CO (2006) Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Journal of geophysical research: Biogeosciences, 111(G2)Google Scholar
  16. 16.
    Gonzalez RC, Woods RE (2007) Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  17. 17.
    Guttman N, Julesz B (1963) Lower limits of auditory periodicity analysis. The Journal of the Acoustical Society of America 35(4):610–610CrossRefGoogle Scholar
  18. 18.
    Hudhud GAA, Turner MJ (2005) Digital removal of power frequency artifacts using a Fourier space median filter. IEEE Signal Processing Letters 12(8):573–576CrossRefGoogle Scholar
  19. 19.
    Ji TY, Lu Z, Wu QH (2007) Optimal soft morphological filter for periodic noise removal using a particle swarm optimiser with passive congregation. Signal Process 87(11):2799–2809CrossRefGoogle Scholar
  20. 20.
    Ji Z, et al (2006) Simple and efficient soft morphological filter in periodic noise reduction’. In: TENCON 2006–2006 IEEE Region 10 Conference, IEEE, pp. 1–4Google Scholar
  21. 21.
    Jing W, Liu DC (2010) 2-D FFT for periodic noise removal on strain image. International Conference on Bioinformatics and Biomedical Engineering, ChinaGoogle Scholar
  22. 22.
    Ketenci S, Gangal A (2012) Design of Gaussian star filter for reduction of periodic noise and quasi-periodic noise in gray level images. In Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on IEEE, pp. 1–5Google Scholar
  23. 23.
    Kinney JH, Nichols MC (1992) X-ray tomographic microscopy (XTM) using synchrotron radiation. Annu Rev Mater Sci 22(1):121–152CrossRefGoogle Scholar
  24. 24.
    Konstantinidis AC et al (2010) Optical characterisation of a CMOS active pixel sensor using periodic noise reduction techniques. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 620(2):549–556CrossRefGoogle Scholar
  25. 25.
    Koukou V, et al (2015) Dual Energy Method for Breast Imaging: A Simulation Study. Computational and Mathematical Methods in Medicine, pp. 1–8CrossRefGoogle Scholar
  26. 26.
    Kumar V, Gupta P (2012) Importance of statistical measures in digital image processing. International Journal of Emerging Technology and Advanced Engineering 2(8) ISSN 2250-2459Google Scholar
  27. 27.
    Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. John Wiley & Sons, HobokenGoogle Scholar
  28. 28.
    Mnih V, Hinton GE (2012) Learning to label aerial images from noisy data’. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp. 567–574Google Scholar
  29. 29.
    Moallem P et al (2010) Adaptive Optimum Notch Filter for Periodic Noise Reduction in Digital Images. Amirkabir International Journal of Electrical & Electronics Engineering 42(1):1–7Google Scholar
  30. 30.
    Moallem P et al (2015) A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image. SIViP 9(5):1179–1191CrossRefGoogle Scholar
  31. 31.
    Pitas I, Venetsanopoulos AN (2013) Nonlinear digital filters: principles and applications, vol 84. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  32. 32.
    Puschner P, Burns A (2000) Guest editorial: A review of worst-case execution-time analysis. Real-Time Systems 18(2):115–128CrossRefGoogle Scholar
  33. 33.
    Reichelt R (2007) Scanning electron microscopy. In: Science of microscopy. Springer, New York, pp 133–272CrossRefGoogle Scholar
  34. 34.
    Rindfleisch TC et al (1971) Digital processing of the Mariner 6 and 7 pictures. J Geophys Res 76(2):394–417CrossRefGoogle Scholar
  35. 35.
    Saravanakumar S (2013) Removal of Moiré Pattern Noise in Images using Median and Gaussian Filter. International Journal of Science, Engineering and Technology Research 2(2):380–385Google Scholar
  36. 36.
    Schowengerdt R (2006) Remote sensing: models and methods for image processing. Academic Press, OrlandoGoogle Scholar
  37. 37.
    Schubert PC (1986) Periodic image artifacts from continuous-tone laser scanners. Appl Opt 25(21):3880–3884CrossRefGoogle Scholar
  38. 38.
    Smith RD (2012) Digital transmission systems, 3rd edn. Springer Science & Business Media, HeidelbergGoogle Scholar
  39. 39.
    Sugiura Y, et al (2013) A comb filter with adaptive notch bandwidth for periodic noise reduction’. In Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on IEEE, pp. 1–4Google Scholar
  40. 40.
    Sur F (2015) An a-contrario approach to quasi-periodic noise removal. In: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, pp. 3841–3845Google Scholar
  41. 41.
    Sur F, Grédiac M (2015) Automated removal of quasiperiodic noise using frequency domain statistics. Journal of Electronic Imaging 24(1):013003_1–013003_19CrossRefGoogle Scholar
  42. 42.
    Torralba A, Oliva A (2003) Statistics of natural image categories. Netw Comput Neural Syst 14(3):391–412CrossRefGoogle Scholar
  43. 43.
    Van der Schaaf VA, Van Hateren JV (1996) Modelling the power spectra of natural images: statistics and information. Vis Res 36(17):2759–2770CrossRefGoogle Scholar
  44. 44.
    Varghese J (2016) Adaptive threshold based frequency domain filter for periodic noise reduction. AEU-International Journal of Electronics and Communications 70(12):1692–1701CrossRefGoogle Scholar
  45. 45.
    Varghese J et al (2016) Laplacian-Based Frequency Domain Filter for the Restoration of Digital Images Corrupted by Periodic Noise. Can J Electr Comput Eng 39(2):82–91CrossRefGoogle Scholar
  46. 46.
    Varghese J et al (2016) Fourier transform-based windowed adaptive switching minimum filter for reducing periodic noise from digital images. IET Image Process 10(9):646–656CrossRefGoogle Scholar
  47. 47.
    Vaseghi VS (2013) Advanced signal processing and digital noise reduction, 3rd edn. Wiley, New JerseyGoogle Scholar
  48. 48.
    Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transaction on Image Processing 13(4):600–612CrossRefGoogle Scholar
  49. 49.
    Wei Z et al (2012) A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image. Micron 43(2):170–176CrossRefGoogle Scholar
  50. 50.
    Wilford J, Creasey J (2002) Landsat thematic mapper: Geophysical and remote sensing methods for regolith exploration. (Ed. E Papp), pp. 6–12Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringIIESTHowrahIndia

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