Skip to main content

Advertisement

Springer Nature Link
Log in

Efficient non-local means denoising for image sequences with dimensionality reduction

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

Abstract

The aim of this paper is to improve both accuracy and computational efficiency of non-local means video (NLMV) denoising algorithm. A technique of principal component analysis (PCA) is used to reduce the heavy dimensionality of patches. A pre-processing step of shot boundary detection is used to split the video sequence into different shots having content-wise similar frames. Further PCA is computed globally for these shots. To speed-up the denoising process, weights are computed in reduced subspace. In the proposed method, we modify the original histogram difference (HD) technique such that content-wise similar frames are separated more systematically and accurately. We have achieved improvement with respect to accuracy and computational speed compared to standard NLM. Moreover, qualitative and quantitative comparisons show that the proposed method is consistently superior compared to that of NLM and some of its variants.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Almahdi R, Hardie RC (2016) Recursive non-local means filter for video denoising with Poisson-Gaussian noise. IEEE National Aerospace and Elect Conf (NAECON) and Ohio Inn Summ (OIS) 318-322

  2. Azzabou N, Paragios N, Guichard F (2007) Image denoising based on adapted dictionary Computation. In Proc., IEEE Int’l Conf. on Image Process, vol 3, p 109–112

  3. Balster EJ, Zheng YF, Ewing RL (2006) Combined spatial and temporal domain wavelet Shrinkage algorithm for video denoising. IEEE Trans Circuits Syst Video Technol 16(2):220–230

    Article  Google Scholar 

  4. Bhujle H, Chaudhuri S (2014) Novel speed-up strategies for NLM denoising with patch and edge patch based dictionaries. IEEE Trans Image Process 23(1):356–365

    Article  MathSciNet  Google Scholar 

  5. Boreczky JS, Rowe LA (1996) Comparison of video shot boundary detection techniques. Journal of Electronic Imaging 5(2):122–128

    Article  Google Scholar 

  6. Boulanger J, Kervrann C, Bouthemy P (2007) Space-time adaptation for patch-based image sequence restoration. IEEE Trans Pattern Anal Mach Intell 29(6):1096–1102

    Article  Google Scholar 

  7. Brailean J, Kleihorst R, Efstratiadis S, Katsaggelos A, Lagendijk R (1995) Noise reduction filters fordynamic image sequences: A review. Proc IEEE 83:1272–1281

    Article  Google Scholar 

  8. Brox T, KleinSchmidt O, Cremers D (2008) Efficient non-local means for denoising of textural Patterns. IEEE Trans Image Process 17(7):1083–1092

    Article  MathSciNet  Google Scholar 

  9. Bruni V, Vitulano D (2007) Combined image compression and denoising using wavelets. Signal Process Image Commun 22:86–101

    Article  Google Scholar 

  10. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. Proc IEEE Comp Vis and Patt Recogn, p 60–65

  11. Buades A, Coll B, Morel JM (2008) Nonlocal image and movie denoising. Int J Comput Vis 76(2):123–140

    Article  Google Scholar 

  12. Campisi P, Neri A, Sorgi L (2002) Automatic dissolve and fade detection for video sequences. Proc., Intl. Conf. on Dig Sig Process, vol 2, p 567–570

  13. Cernekova Z, Kotropoulos C, Pitas I (2003) Video shot segmentation using singular valuedecomposition. Proc., Acoustics, speech and signprocess, vol 3, p 181–184

  14. Cho D, Bui TD (2005) Multivariate statistical modelling for image denoising using wavelet Transforms. Signal Process Image Commun 20:77–89

    Article  Google Scholar 

  15. Dabov K, Foi A, Egiazarian K (2007) Video denoising by space 3D transform-domain collaborative filtering. In Proc., European Signal Process. Conf, p 145-149

  16. Dekeyser F, Bouthemy P, Perez P (2000) Spatiotemporal Wiener filtering of image sequences usinga parametric motion model. Proc IEEE Int’l Conf. on Image Process, vol 1, p 208–211

  17. Dugad R, Ahuja N (1999) Video denoising by combining Kalman and Wiener estimates. Intl. Conf. on Image Process, vol 4, p 152–156

  18. Feng H, Fang W, Liu S, Fang Y (2005) A new general framework for shot boundary detection andkey-frame extraction. Proc., ACM Sig intl. work on Mult., p 121–126

  19. Gargi U, Kasturi R, Strayer S (2000) Performance characterization of video-shot-change detection methods. IEEE Trans Circuits Syst Video Technol 10(1):1–13

    Article  Google Scholar 

  20. Ghosh S, Chaudhuri KN (2016) Fast separable non-local means. Journal of Electronic Imaging 25(2):023026

    Article  Google Scholar 

  21. Gordienko Y, Kochura Y, Alienin O, Rokovyi O, Stirenko S, Gang P, Hui J, Zeng W (2018) Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer. International Conference on Advanced Computational Intelligence arXiv preprint arXiv:1801.06495

  22. Guo L, Au OC, Ma M, Liang Z (2010) Fast Multi-Hypothesis Motion Compensated Filter for video Denoising. J Signal Process Syst 60(3):273–290

    Article  Google Scholar 

  23. Hanjalic A (2002) Shot-boundary detection: unraveled and resolved? IEEE Trans Circuits Syst Video Technol 12(2):90–105

    Article  Google Scholar 

  24. Hao B, Li M, Feng X (2008) Wavelet iterative regularization for image restoration with varying scale Parameter. Signal Process Image Commun 23:433–441

    Article  Google Scholar 

  25. Heng W, Ngan K (1999) The implementation of object-based shot boundary detection using edge tracing and tracking. Proc., IEEE Intl. Symp on Circ and Sys, vol 4, p 439–442

  26. Hosseini H, Marvasti F (2013) Fast restoration of natural images corrupted by high-density impulse noise. EURASIP J Image Video Process 2013:15

    Article  Google Scholar 

  27. Hu J, Zhou J, Wu X (2016) Non-local MRI denoising using random sampling. Magn Reson Imaging 34:990–999

    Article  Google Scholar 

  28. Kalra GS, Singh S (2016) Efficient digital image denoising for gray scale images. Journal of Multimedia Tools and Applications 75(8):4467–4484

    Article  Google Scholar 

  29. Kervrann C, Boulanger J, Coupe P (2007) Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. In Proc., Conf. on Scale-Space and Var Meth in Compu Vis, p 520–532

  30. Khazron PA, Selesnick IW (2010) Spatiotemporal wavelet maximum a posteriori estimation for video denoising. Journal of Electronic Imaging 19(4):043015. https://doi.org/10.1117/1.3514739

  31. Kleihorst RP, Lagendijk RL, Biemond J (1995) Noise reduction of image sequences using motion Compensation and signal decomposition. IEEE Trans Image Process 4(3):274–284

    Article  Google Scholar 

  32. Lee CM, Ip MC (1994) A robust approach for camera break detection in color video sequence. Proc., IAPR work on mach vis appl, p 502–505

  33. Lee SH, Kang MG (1998) Spatiotemporal video filtering algorithm based on 3D anisotropic diffusion equation. Proc IEEE Intl Conf Image Process, vol 2, p 447–450

  34. Lee TH, Kang JK, Song BC (2012) Video denoising using overlapped motion compensation and advanced collaborative filtering. Journal of Electronic Imaging 21(2):023004. https://doi.org/10.1117/1.JEI.21.2.023004

  35. Lelescu D, Schonfeld D (2003) Statistical sequential analysis for real-time video scene change detection on compressed multimedia bit stream. IEEE Transactions on Multimedia 5(1):106–117

    Article  Google Scholar 

  36. Li X, Zheng Y (2009) Patch-based video processing: A variational Bayesian approach. IEEE Trans Circuits Syst Video Technol 19(1):27–40

    Article  Google Scholar 

  37. Li W, Yin X, Liu Y, Zhang M (2016) Robust video denoising for mixed Poisson, Gaussian and impulse noise. IEEE Intl. Conf. on Image Vision and Computing, vol 318, p 322

  38. Lia W, Zhang J, Dai Q (2011) Video denoising using shape-adaptive sparse representation over Similar spatio-temporal patches. Signal Process Image Commun 26(4):250–265

    Article  Google Scholar 

  39. Liu Z, Wang T (2016) An adaptive image denoising algorithm based on wavelet transform and independent component analysis. In Proc., IEEE Intl. Conf. on Intell. Sys Design and Engg. Appl., p 104-107

  40. Mahmoudi M, Sapiro G (2005) Fast image and video denoising via nonlocal means of similar Neighbourhoods. IEEE Sign Process Lett 12:839–842

    Article  Google Scholar 

  41. Mélange T, Nachtegael M, Kerre EE, Zlokolica V, Schulte S, Witte VD, Pižurica A, Philips W (2008) Video denoising by fuzzy motion and detail adaptive averaging. Journal of Electronic Imaging 19(4):043005. https://doi.org/10.1117/1.2992065

  42. Mohamadi N, Soheili AR, Toutounian F (2017) A new hybrid denoising model based on PDEs. Journal of Multimedia Tools and Applications 74(24):1–16

    Google Scholar 

  43. Muresan DD, Parks TW (2003) Adaptive principal components and image denoising. Proc. Intl. Conf. Image Process, vol 1, p 101–104

  44. Naser AM (2016) Color to grayscale image conversion based dimensionality reduction with stationary wavelet transform. In Proc., IEEE Intl. Conf. on Mult in IT and Com Sci and Appl., p 1-5

  45. Orchard J, Ebrahim M, Wang A (2008) Efficient nonlocal means denoising using the SVD. In Proc., IEEE Int’l Conf. on Image Process, p 1732-735

  46. Pierrick C, Pierre Y, Christian B (2006) Fast non local means denoising for 3D MR images. In Proc., Intl. Conf. on Med Image Compu and Compu-Ass Int, p 33–40

  47. Selesnick IW, Li KY (2003) Video denoising using 2D and 3D dual- tree complex wavelet transforms. Proc Wavelet Appl on Sig and Image Process, p 607–618

  48. Tasdizen T (2008) Principal components for nonlocal means image denoising. In Proc., IEEE Int’l Conf. on Image Process, p 1728–1731

  49. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In Proc., Sixth Intl. Conf. on Computer Vision, p 839–846

  50. Vignesh R, Byung OT, Kuo CC (2010) Fast non-local means computation with probabilistic early termination. IEEE Sign Process Lett 17(3):277–280

    Article  Google Scholar 

  51. Wang XY, Liu YC, Zhang N, Wu CJ, Yang HY (2015) An edge preserving adaptive image Denoising. Journal of Multimedia Tools and Applications 74(24):11703–11720

    Article  Google Scholar 

  52. Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232–242

    Article  Google Scholar 

  53. Xu D, Wang X, Sun G, Li H (2015) Towards a novel image denoising method with edge preserving sparse representation based on Laplacian of B-Spline edge detection. Journal of Multimedia Tools and Applications l76(17):17839–17854

    Article  Google Scholar 

  54. Xu BH, Gang Y, Wei Z, Cen Y, Zhao RZ, Miao ZJ, Yao Z, Wilson R (2016) Video restoration based on Patch Match and reweighted low-rank mat x recovery. Journal of Multimedia Tools and Applications 75(5):2681–2696

    Article  Google Scholar 

  55. Yuan J, Li J, Lin F, Zhang B (2005) An unified shot boundary detection framework based on graph partition model. Proc., the 13th annual ACM intl. conf. on Mult., p 539–542

  56. Zhang HJ, Kankanhalli A, Smoliar SW (2001) Automatic partitioning of full motion video. Read in multcompu and net, p 321–338

  57. Zhang J, Li M, He Y (2015) \( {S}_2^1 \) norm based sparse and low-rank decomposition scheme for video denoising. Journal of Electronic Imaging 24(4):043013. https://doi.org/10.1117/1.JEI.24.4.043013

  58. Zhu Z, You X, Chen CL, Tao D, Ou W, Tiang X, Zou T (2015) An adaptive hybrid pattern for noise-robust texture analysis. Pattern Recogn 48:2592–2608

    Article  Google Scholar 

  59. Zlokolica V, Philips W (2004) Motion and detail adaptive denoising of video. In Proc IS&T/SPIE Sym on Electr Imag, p 1417–421

  60. Zlokolica V, Pižurica A, Philips W, Schulte S, Kerre E (2006) Fuzzy logic recursive motion detection and denoising of video sequence. Journal of Electronic Imaging 15(2):023008. https://doi.org/10.1117/1.2201548

Download references

Author information

Authors and Affiliations

  1. Department of Electronics & Communication Engineering, SDM College of Engineering & Technology, Dhavalagiri, Dharwad, Karnataka State, 580002, India

    Hemalata Bhujle & Basavaraj H. Vadavadagi

  2. Indian Institute of Technology Bombay, Mumbai, India

    Shivanand Galaveen

Authors
  1. Hemalata Bhujle
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Basavaraj H. Vadavadagi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shivanand Galaveen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hemalata Bhujle.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhujle, H., Vadavadagi, B.H. & Galaveen, S. Efficient non-local means denoising for image sequences with dimensionality reduction. Multimed Tools Appl 77, 30595–30613 (2018). https://doi.org/10.1007/s11042-018-6159-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6159-2

Keywords