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Speeding up the patch ordering method for image denoising

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Abstract

Smooth ordering of local patches (patch ordering) has been shown to give state-of-the-art results for image denoising. However, use of very large TSPs (Traveling Salesman Problem) makes it computationally intensive. The patch ordering method forms two large TSPs and employs their approximate solutions in a filtering process to perform denoising. On average, 84% of patch ordering’s execution time was found to be spent on solving TSPs. A variation of the patch ordering method is proposed with two changes. First, numerous smaller TSPs are formed instead of two large ones. Second, the filtering process is modified to perform denoising with solutions of numerous smaller TSPs instead of two large TSPs. Compared to the patch ordering method, the proposed method can denoise images 3.34 times faster. In terms of PSNR, the proposed method’s denoising performance differed by only 0.06 dB on average. Moreover, the proposed method is highly amenable to parallelization. By solving TSPs in parallel, the proposed method’s parallel implementation denoised images 4.89 times faster using four CPU cores which reduced denoising time by 80%. Also shown is that given the same computing resources (CPU cores), the proposed method shall attain speedups higher than those by a similarly parallelized version of the patch ordering method. The proposed approach can be used to speed up patch ordering for other image processing tasks.

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References

  1. Berry MW, Browne M (2006) Lecture notes in data mining. World Scientific

  2. Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: a fresh approach to numerical computing. SIAM Rev 59(1):65–98. https://doi.org/10.1137/141000671

    Article  MathSciNet  MATH  Google Scholar 

  3. Chang X, Yu YL, Yang Y, Xing EP (2017) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell 39(8):1617–1632

    Article  Google Scholar 

  4. Chatterjee P, Milanfar P (2012) Patch-based near-optimal image denoising. IEEE Trans Image Process 21(4):1635–1649. https://doi.org/10.1109/TIP.2011.2172799

    Article  MathSciNet  MATH  Google Scholar 

  5. Cormen TH (2009) Introduction to algorithms. MIT press

  6. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16 (8):2080–2095. https://doi.org/10.1109/TIP.2007.901238

    Article  MathSciNet  Google Scholar 

  7. Dasgupta C, Vazirani UV (2015) Algorithms

  8. Fan Q, Zhang L (2018) A novel patch matching algorithm for exemplar-based image inpainting. Multimed Tools Appl 77(9):10,807–10,821. https://doi.org/10.1007/s11042-017-5077-z

    Article  Google Scholar 

  9. Hennessy J, Patterson D (2011) Computer architecture: a quantitative approach. The Morgan Kaufmann Series in Computer Architecture and Design Elsevier Science

  10. Kozen DC (2012) The design and analysis of algorithms. Springer Science & Business Media

  11. Li Z, Nie F, Chang X, Yang Y (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Trans Knowl Data Eng 29(10):2100–2110

    Article  Google Scholar 

  12. Lin FJ, Chuang JH (2017) Image super-resolution by estimating the enhancement weight of self example and external missing patches. Multimed Tools Appl, 1–17. https://doi.org/10.1007/s11042-017-5350-1

  13. Liu C, Chen Q, Li H (2017) Single image super-resolution reconstruction technique based on a single hybrid dictionary. Multimed Tools Appl 76 (13):14,759–14,779. https://doi.org/10.1007/s11042-016-4022-x

    Article  Google Scholar 

  14. Luo E, Chan SH, Nguyen TQ (2015) Adaptive image denoising by targeted databases. IEEE Trans Image Process 24(7):2167–2181. https://doi.org/10.1109/TIP.2015.2414873

    Article  MathSciNet  MATH  Google Scholar 

  15. Ma Z, Chang X, Xu Z, Sebe N, Hauptmann AG (2018) Joint attributes and event analysis for multimedia event detection. IEEE Trans Neural Netw Learn Syst 29(7):2921–2930

    MathSciNet  Google Scholar 

  16. Milanfar P (2013) A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Signal Process Mag 30(1):106–128. https://doi.org/10.1109/MSP.2011.2179329

    Article  Google Scholar 

  17. Navarro CA, Hitschfeld-Kahler N, Mateu L (2014) A survey on parallel computing and its applications in data-parallel problems using GPU architectures. Commun Comput Phys 15(02):285–329. https://doi.org/10.4208/cicp.110113.010813a

    Article  MathSciNet  MATH  Google Scholar 

  18. Pacheco P (2011) An introduction to parallel programming. Elsevier

  19. Padua D (2011) Encyclopedia of parallel computing. Springer Science & Business Media

  20. Papyan V, Elad M (2016) Multi-scale patch-based image restoration. IEEE Trans Image Process 25(1):249–261. https://doi.org/10.1109/TIP.2015.2499698

    Article  MathSciNet  MATH  Google Scholar 

  21. Paschos VT (2013) Paradigms of combinatorial optimization: problems and new approaches, vol 2. Wiley

  22. Ram I, Elad M, Cohen I (2013) Image processing using smooth ordering of its patches. IEEE Trans Image Process 22(7):2764–2774. https://doi.org/10.1109/TIP.2013.2257813

    Article  MathSciNet  MATH  Google Scholar 

  23. Rosen KH, Krithivasan K (2012) Discrete mathematics and its applications: with combinatorics and graph theory. Tata McGraw-Hill Education

  24. Xu B, Cui Y, Zuo B, Yang J, Song J (2016) Polarimetric SAR image filtering based on patch ordering and simultaneous sparse coding. IEEE Trans Geosci Remote Sens 54(7):4079–4093. https://doi.org/10.1109/TGRS.2016.2536648

    Article  Google Scholar 

  25. Yang X, Sun Q, Wang T (2018) Image quality assessment improvement via local gray-scale fluctuation measurement. Multimed Tools Appl, 1–18. https://doi.org/10.1007/s11042-018-5740-z

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan

    Badre Munir & Syed Fawad Hussain

  2. Machine Learning and Data Science (MDS) Lab, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan

    Syed Fawad Hussain

  3. Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan

    Adnan Noor

Authors
  1. Badre Munir
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  2. Syed Fawad Hussain
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  3. Adnan Noor
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Correspondence to Badre Munir.

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Munir, B., Hussain, S.F. & Noor, A. Speeding up the patch ordering method for image denoising. Multimed Tools Appl 78, 23639–23657 (2019). https://doi.org/10.1007/s11042-019-7708-z

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  • DOI: https://doi.org/10.1007/s11042-019-7708-z

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