Circuits, Systems, and Signal Processing

, Volume 38, Issue 7, pp 3269–3294 | Cite as

An FPGA-Oriented Algorithm for Real-Time Filtering of Poisson Noise in Video Streams, with Application to X-Ray Fluoroscopy

  • G. Castellano
  • D. De Caro
  • D. EspositoEmail author
  • P. Bifulco
  • E. Napoli
  • N. Petra
  • E. Andreozzi
  • M. Cesarelli
  • A. G. M. Strollo


In this paper we propose a new algorithm for real-time filtering of video sequences corrupted by Poisson noise. The algorithm provides effective denoising (in some cases overcoming the filtering performances of state-of-the-art techniques), is ideally suited for hardware implementation, and can be implemented on a small field-programmable gate array using limited hardware resources. The paper describes the proposed algorithm, using X-ray fluoroscopy as a case study. We use IIR filters for time filtering, which largely simplifies hardware cost with respect to previous FIR filter-based implementations. A conditional reset is implemented in the IIR filter, to minimize motion blur, with the help of an adaptive thresholding approach. Spatial filtering performs a conditional mean to further reduce noise and to remove isolated noisy pixels. IIR filter hardware implementation is optimized by using a novel technique, based on Steiglitz–McBride iterative method, to calculate fixed-point filter coefficients with minimal number of nonzero elements. Implementation results using the smallest StratixIV FPGA show that the system uses only, at most, the 22% of the resources of the device, while performing real-time filtering of 1024 × 1024@49fps video stream. For comparison, a previous FIR filter-based implementation, on the same FPGA, in the same conditions and constraints (1024 × 1024@49fps), requires the 80% of the logic resources of the FPGA.


Real-time video filtering IIR filtering IIR filter design Poisson noise X-ray videofluoroscopy processing Field-programmable gate array (FPGA) 



  1. 1.
    R. Aufrichtig, D.L. Wilson, X-ray fluoroscopy spatio-temporal filtering with object detection. IEEE Trans. Med. Imaging 14(4), 733–746 (1995)Google Scholar
  2. 2.
    S.P. Awate, R.T. Whitaker, Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 44–51 (2005)Google Scholar
  3. 3.
    E.J. Balster, Y.F. Zheng, R.L. Ewing, Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising. IEEE Trans. Circuits Syst. Video Technol. 16(2), 220–230 (2006)Google Scholar
  4. 4.
    A.A. Bindilatti, N.D.A. Mascarenhas, A non local Poisson denoising algorithm based on stochastic distances. IEEE Signal Process. Lett. 20(11), 1010–1013 (2013)Google Scholar
  5. 5.
    D. Bhonsle, V. Chandra, G.R. Sinha, Medical image denoising using bilateral filter. Int. J. Image Graph. Signal Process. 4(6), 36–43 (2012)Google Scholar
  6. 6.
    S. Bonettini, V. Ruggiero, An alternating extra gradient method for total variation-based image restoration from Poisson data. Inverse Probl. 27(9), 1–28 (2011)zbMATHGoogle Scholar
  7. 7.
    A. Buades, B. Coll, J.M. Morel, A non-local algorithm for image denoising. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 60–65 (2005)Google Scholar
  8. 8.
    J. Cai, B. Dong, S. Osher, Z. Shen, Image restoration: total variation, wavelet frames, and beyond. J. Am. Math. Soc. 25, 1033–1089 (2012)MathSciNetzbMATHGoogle Scholar
  9. 9.
    T. Cerciello, P. Bifulco, M. Cesarelli, L. Paura, G. Pasquariello, R. Allen, Noise reduction in fluoroscopic image sequences for joint kinematics analysis. In Proceedings of 22nd Mediterranean Conference on Medical and Biological Engineering and Computing, vol. 29, pp. 323–326 (2010)Google Scholar
  10. 10.
    T. Cerciello, M. Romano, P. Bifulco, M. Cesarelli, R. Allen, Advanced template matching method for estimation of intervertebral kinematics of lumbar spine. Med. Eng. Phys. 33(10), 1293–1302 (2011)Google Scholar
  11. 11.
    T. Cerciello, P. Bifulco, M. Cesarelli, A. Fratini, A comparison of denoising methods for X-ray fluoroscopic images. Biomed. Signal Process. Control 7(6), 550–559 (2012)Google Scholar
  12. 12.
    M. Cesarelli, P. Bifulco, T. Cerciello, M. Romano, L. Paura, X-ray fluoroscopy noise modeling for filter design. Int. J. Comput. Assist. Radiol. Surg. 8(2), 269–278 (2013)Google Scholar
  13. 13.
    C.L. Chan, A.K. Katsaggelos, A.V. Sahakian, Image sequence filtering in quantum-limited noise with applications to low-dose fluoroscopy. IEEE Trans. Med. Imaging 12(3), 610–621 (1993)Google Scholar
  14. 14.
    K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetGoogle Scholar
  15. 15.
    K. Dabov, A. Foi, K. Egiazarian, Video denoising by sparse 3D transform-domain collaborative filtering. In Proceedings of European Signal Processing Conference (EUSIPCO), Poznań, Poland, Sept. 3–7 (2007)Google Scholar
  16. 16.
    M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetGoogle Scholar
  17. 17.
    M.A.T. Figueiredo, J.M. Bioucas-Dias, Restoration of Poissonian images using alternating direction optimization. IEEE Trans. Image Process. 19(12), 3133–3145 (2010)MathSciNetzbMATHGoogle Scholar
  18. 18.
    A. Foi, Clipped noisy images: heteroskedastic modeling and practical denoising. Signal Process. 89(12), 2609–2629 (2009)zbMATHGoogle Scholar
  19. 19.
    M. Genovese, P. Bifulco, D. De Caro, E. Napoli, N. Petra, M. Romano, M. Cesarelli, A.G.M. Strollo, Hardware implementation of a spatio-temporal average filter for real-time denoising of fluoroscopic images. Integr. VLSI J. 49, 114–124 (2015)Google Scholar
  20. 20.
    M. Ghazal, A. Amer, A. Ghrayeb, Structure-oriented multidirectional Wiener filter for denoising of image and video signals. IEEE Trans. Circuits Syst. Video Technol. 18(12), 1797–1802 (2008)Google Scholar
  21. 21.
    R.M. Harrison, C.J. Kotre, Noise and threshold contrast characteristics of a digital fluoroscopic system. Phys. Med. Biol. 31(5), 512–586 (1986)Google Scholar
  22. 22.
    V.M. Hubbard, The approximation of a Poisson distribution by a Gaussian distribution. Proc. IEEE 58(9), 1374–1375 (1970)Google Scholar
  23. 23.
    A. Jiang, H.K. Kwan, Minimax design of IIR digital filters using iterative SOCP. IEEE Trans. Circuits Syst. I Reg. Pap. 57(6), 1326–1337 (2010)MathSciNetGoogle Scholar
  24. 24.
    S.M. Kuo, B.H. Lee, Real-time digital signal processing (Wiley, New York, 2001)Google Scholar
  25. 25.
    M.C. Lang, Least-squares design of IIR filters with prescribed magnitude and phase responses and a pole radius constraint. IEEE Trans. Signal Process. 48(11), 3109–3121 (2000)Google Scholar
  26. 26.
    C.M. Lo, A.A. Sawchuk, Nonlinear restoration of filtered images with Poisson noise. Proc. SPIE 207, 84–95 (1979)Google Scholar
  27. 27.
    F. Luisier, T. Blu, M. Unser, Image denoising in mixed Poisson–Gaussian noise. IEEE Trans. Image Process. 20(3), 696–708 (2011)MathSciNetzbMATHGoogle Scholar
  28. 28.
    M. Mäkitalo, A. Foi, Optimal inversion of the Anscombe transformation in low-count Poisson image denoising. IEEE Trans. Image Process. 20(1), 99–109 (2011)MathSciNetzbMATHGoogle Scholar
  29. 29.
    M. Mäkitalo, A. Foi, Optimal inversion of the generalized Anscombe transformation for Poisson–Gaussian noise. IEEE Trans. Image Process. 22(1), 91–103 (2013)MathSciNetzbMATHGoogle Scholar
  30. 30.
    S. Mishra, P.D. Swami, Spatio-temporal video denoising by block-based motion detection. Int. J. Eng. Trends Technol. 4(8), 3371–3382 (2013)Google Scholar
  31. 31.
    M. Moradi, S.S. Mahdavi, E. Dehghan, J.R. Lobo, S. Deshmukh, W.J. Morris, G. Fichtinger, S.E. Salcudean, Seed localization in ultrasound and registration to C-arm fluoroscopy using matched needle tracks for prostate brachy therapy. IEEE Trans. Biomed. Eng. 59(9), 2558–2567 (2012)Google Scholar
  32. 32.
    R.B. Paranjape, Fundamental enhanced techniques, in Handbook of Medical Imaging, ed. by I.N. Bankman (Academic Press, London, 2000)Google Scholar
  33. 33.
    T.W. Parks, C.S. Burrus, Digital Filter Design (Wiley, New York, 1987), pp. 226–228zbMATHGoogle Scholar
  34. 34.
    J.G. Proakis, D.G. Manolakis, Digital Signal Processing Principles, Algorithms, and Applications, 3rd edn. (Prentice-Hall, Englewood Cliffs, 1996)Google Scholar
  35. 35.
    G. Steidl, J. Weickert, T. Brox, P. Mrzek, M. Welk, On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and sides. SIAM J. Numer. Anal. 42(2), 686–713 (2004)MathSciNetzbMATHGoogle Scholar
  36. 36.
    K. Steiglitz, L.E. McBride, A technique for the identification of linear system. IEEE Trans. Autom. Control AC-10, 461–464 (1965)Google Scholar
  37. 37.
    M.J. Tapiovaara, SNR and noise measurements for medical imaging: II. Application to fluoroscopic X-ray equipment. Phys. Med. Biol. 38(12), 1761–1788 (1993)Google Scholar
  38. 38.
    M. Tomic, S. Loncaric, D. Sersic, Adaptive spatio-temporal denoising of fluoroscopic X-ray sequences. Biomed. Signal Process. Control 7(2), 173–179 (2012)Google Scholar
  39. 39.
    G. Varghese, Z. Wang, Video denoising based on a spatiotemporal Gaussian scale mixture model. IEEE Trans. Circuits Syst. Video Technol. 20(7), 1032–1040 (2010)Google Scholar
  40. 40.
    J. Wang, T.J. Blackburn, The AAPM/RSNA physics tutorial for residents: X-ray image intensifiers for fluoroscopy. Radiographics 20(5), 1471–1477 (2000)Google Scholar
  41. 41.
    Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)Google Scholar
  42. 42.
    T. Yamazaki, T. Watanabe, Y. Nakajima, K. Sugamoto, T. Tomita, H. Yoshikawa, S. Tamura, Improvement of depth position in 2-D/3-D registration of knee implants using single-plane fluoroscopy. IEEE Trans. Med. Imaging 23(5), 602–612 (2004)Google Scholar
  43. 43.
    Y.-L. You, M. Kaveh, Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 9(10), 1723–1730 (2000)MathSciNetzbMATHGoogle Scholar
  44. 44.
    B. Zhang, J.M. Fadili, J.-L. Starck, Wavelets, ridgelets, and curvelets for Poisson noise removal. IEEE Trans. Image Process. 17(7), 1093–1108 (2008)MathSciNetGoogle Scholar
  45. 45.
    V. Zlokolica, A. Pizurica, W. Philips, Wavelet-domain video denoising based on reliability measures. IEEE Trans. Circuits Syst. Video Technol. 16(8), 993–1007 (2006)Google Scholar
  46. 46.
    V. Zlokolica, A. Pižurica, W. Philips, S. Schulte, E. Kerre, Fuzzy logic recursive motion detection and denoising of video sequences. J. Electron. Imaging 15(2), 1–13 (2006)Google Scholar
  47. 47.
    C. Zuo, Y. Liu, X. Tan, W. Wang, M. Zhang, Video denoising based on a spatiotemporal Kalman-bilateral mixture model. Sci. World J. 2013, 438147 (2013)Google Scholar
  48. 48.
    Video Sequence Database [Online] (2000). Accessed July 2016
  49. 49.
    Gurobi Optimizer Reference Manual (2000). Accessed Nov 2014

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information TechnologyUniversity of Napoli Federico IINaplesItaly

Personalised recommendations