Skip to main content
SpringerLink
Log in

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

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. [Online Available]: http://nullprogram.com/blog/2011/10/13/

  2. [Online Available]: http://users.ecs.soton.ac.uk/km/imaging/course/moire.html

  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. 147–153

  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. 181–191

  5. Aizenberg I, Butakoff C (2008) A windowed Gaussian notch filter for quasi-periodic noise removal. Image Vis Comput 26(10):1347–1353

    Article  Google Scholar 

  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):1–5

    Google Scholar 

  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, DC

    Google Scholar 

  8. Blieberger J, Lieger R (1996) Worst-case space and time complexity of recursive procedures. Real-Time Systems 11(2):115–144

    Article  Google Scholar 

  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):1580–1592

    Article  Google Scholar 

  10. Chang Y et al (2015) Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping. IEEE Trans Image Process 24(6):1852–1866

    Article  MathSciNet  MATH  Google Scholar 

  11. Cornelis B et al (2012) Digital canvas removal in paintings. Signal Process 92(4):1166–1171

    Article  Google Scholar 

  12. Cornelis B et al (2017) Removal of canvas patterns in digital acquisitions of paintings. IEEE Trans Image Process 26(1):160–171

    Article  MathSciNet  MATH  Google Scholar 

  13. Eaton P, West P (2010) Atomic force microscopy. Oxford University Press

  14. Fehrenbach J, Weiss P, Lorenzo C (2012) Variational algorithms to remove stationary noise: applications to microscopy imaging. IEEE Trans Image Process 21(10):4420–4430

    Article  MathSciNet  MATH  Google Scholar 

  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)

  16. Gonzalez RC, Woods RE (2007) Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  17. Guttman N, Julesz B (1963) Lower limits of auditory periodicity analysis. The Journal of the Acoustical Society of America 35(4):610–610

    Article  Google Scholar 

  18. Hudhud GAA, Turner MJ (2005) Digital removal of power frequency artifacts using a Fourier space median filter. IEEE Signal Processing Letters 12(8):573–576

    Article  Google Scholar 

  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):2799–2809

    Article  MATH  Google Scholar 

  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. 1–4

  21. Jing W, Liu DC (2010) 2-D FFT for periodic noise removal on strain image. International Conference on Bioinformatics and Biomedical Engineering, China

  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. 1–5

  23. Kinney JH, Nichols MC (1992) X-ray tomographic microscopy (XTM) using synchrotron radiation. Annu Rev Mater Sci 22(1):121–152

    Article  Google Scholar 

  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):549–556

    Article  Google Scholar 

  25. Koukou V, et al (2015) Dual Energy Method for Breast Imaging: A Simulation Study. Computational and Mathematical Methods in Medicine, pp. 1–8

  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 2250-2459

  27. Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. John Wiley & Sons, Hoboken

    Google Scholar 

  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. 567–574

  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):1–7

    Google Scholar 

  30. Moallem P et al (2015) A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image. SIViP 9(5):1179–1191

    Article  Google Scholar 

  31. Pitas I, Venetsanopoulos AN (2013) Nonlinear digital filters: principles and applications, vol 84. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  32. Puschner P, Burns A (2000) Guest editorial: A review of worst-case execution-time analysis. Real-Time Systems 18(2):115–128

    Article  Google Scholar 

  33. Reichelt R (2007) Scanning electron microscopy. In: Science of microscopy. Springer, New York, pp 133–272

    Chapter  Google Scholar 

  34. Rindfleisch TC et al (1971) Digital processing of the Mariner 6 and 7 pictures. J Geophys Res 76(2):394–417

    Article  Google Scholar 

  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):380–385

    Google Scholar 

  36. Schowengerdt R (2006) Remote sensing: models and methods for image processing. Academic Press, Orlando

    Google Scholar 

  37. Schubert PC (1986) Periodic image artifacts from continuous-tone laser scanners. Appl Opt 25(21):3880–3884

    Article  Google Scholar 

  38. Smith RD (2012) Digital transmission systems, 3rd edn. Springer Science & Business Media, Heidelberg

    Google Scholar 

  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. 1–4

  40. Sur F (2015) An a-contrario approach to quasi-periodic noise removal. In: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, pp. 3841–3845

  41. Sur F, Grédiac M (2015) Automated removal of quasiperiodic noise using frequency domain statistics. Journal of Electronic Imaging 24(1):013003_1–013003_19

    Article  Google Scholar 

  42. Torralba A, Oliva A (2003) Statistics of natural image categories. Netw Comput Neural Syst 14(3):391–412

    Article  Google Scholar 

  43. Van der Schaaf VA, Van Hateren JV (1996) Modelling the power spectra of natural images: statistics and information. Vis Res 36(17):2759–2770

    Article  Google Scholar 

  44. Varghese J (2016) Adaptive threshold based frequency domain filter for periodic noise reduction. AEU-International Journal of Electronics and Communications 70(12):1692–1701

    Article  Google Scholar 

  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):82–91

    Article  Google Scholar 

  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):646–656

    Article  Google Scholar 

  47. Vaseghi VS (2013) Advanced signal processing and digital noise reduction, 3rd edn. Wiley, New Jersey

    Google Scholar 

  48. Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transaction on Image Processing 13(4):600–612

    Article  Google Scholar 

  49. Wei Z et al (2012) A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image. Micron 43(2):170–176

    Article  Google Scholar 

  50. Wilford J, Creasey J (2002) Landsat thematic mapper: Geophysical and remote sensing methods for regolith exploration. (Ed. E Papp), pp. 6–12

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, IIEST, Shibpur, Howrah, India

    D. Chakraborty, A. Chakraborty, A. Banerjee & S. R. Bhadra Chaudhuri

Authors
  1. D. Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. R. Bhadra Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Chakraborty.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, D., Chakraborty, A., Banerjee, A. et al. A unified block-based sparse domain solution for quasi-periodic de-noising from different genres of images with iterative filtering. Multimed Tools Appl 78, 26759–26785 (2019). https://doi.org/10.1007/s11042-019-7502-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7502-y

Keywords

Search

Navigation