Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images



In this paper, a parameterless Jaya optimization based neural network filter named as Jaya-functional link multilayer perceptron (Jaya-FLMLP) is proposed for the elimination of Poisson noise from X-ray images. In this proposed adaptive filter, Jaya is applied for updating the weights of the FLMLP network. The proposed neural filter is a combination of a functional link artificial neural network (FLANN) and Multilayer Perceptron (MLP) network. The performance of Jaya-FLMLP is also compared with other five competitive networks such as Wiener, MLP, Least Mean Squares based Functional Link Artificial Neural Network (LMS-FLANN), Particle Swarm Optimization based Functional Link Artificial Neural Network (PSO-FLANN) and Cat Swarm Optimization based Functional Link Artificial Neural Network (CSO-FLANN). The comparison of performance is investigated by the Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Reduction in Decibels (NRDB) values. The simulation results and non-parametric Friedman’s test reveal the superiority of the Jaya-FLMLP filter over others.


Poisson noise X-ray image Adaptive filter Artificial neural network Optimization Friedman’s test 


  1. 1.
    Chen S, Suzuki K (2014) Bone suppression in chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing and consistency processing. Comput Intell Biomed Imaging 33:211–235CrossRefGoogle Scholar
  2. 2.
    Chen F, Wu Y (2015) Improving image recognition by hierarchical model and denoising. 11th International Conference on Natural Computation, 2015Google Scholar
  3. 3.
    Dash PP, Patra D (2011) Evolutionary neural network for noise cancellation in image data. Int J Comput Vis Robot 2:206–217CrossRefGoogle Scholar
  4. 4.
    Dehuri S, Cho SB (2010) Evolutionarily optimized features in functional link neural network for classification. Expert Syst Appl 37:4379–4391CrossRefGoogle Scholar
  5. 5.
    Dokur Z, Olmez T (2002) Segmentation of ultrasound images by using a hybrid neural network. Pattern Recogn Lett 23:1825–1836CrossRefMATHGoogle Scholar
  6. 6.
    Duan H, Wang X (2016) Echo state networks with orthogonal pigeon-inspired optimization for image restoration. IEEE Trans Neural Networks Learn Syst 27:1–13MathSciNetCrossRefGoogle Scholar
  7. 7.
    García DM, Gutiérrez JG, Santos JCR (2012) On the evolutionary optimization of k-NN by label-dependent feature weighting. Pattern Recogn Lett 33:2232–2238CrossRefGoogle Scholar
  8. 8.
    Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 15:178–188CrossRefGoogle Scholar
  9. 9.
    Ginneken BV, Stegmann MB, Loog M (2006) Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database. Med Image Anal 10:19–40CrossRefGoogle Scholar
  10. 10.
    He L, Land R, Greenshields I (2009) A nonlocal maximum likelihood estimation method for rician noise reduction in mr images. IEEE Trans Med Imaging 28:165–172CrossRefGoogle Scholar
  11. 11.
    Kumar M, Mishra SK (2015) Kumar M, Mishra SK (2015) Particle swarm optimization-based functional link artificial neural network for medical image denoising. In: Computational Vision and Robotics. 1st edn, Springer, New Delhi, pp 105–111.
  12. 12.
    Kumar M, Mishra SK (2017) Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed Res 28:4159–4164Google Scholar
  13. 13.
    Kumar M, Mishra SK, Sahu SS (2016) Cat swarm optimization based functional link artificial neural network filter for gaussian noise removal from computed tomography images. Appl Comput Intell Soft Comput 2016:1–6CrossRefGoogle Scholar
  14. 14.
    Li Y, Lu J, Wang L, Fan Y, Li S, Yahagi T (2007) Removing noise from medical CR image using multineural network filter based on noise intensity distribution. Proc. - Third Int. Conf. Nat. Comput, 2007Google Scholar
  15. 15.
    Majhi B, Panda G (2011) Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique. Expert Syst Appl 38(1):321–333CrossRefGoogle Scholar
  16. 16.
    Mirjalili S, Hashim SZM, Sardroudi HM (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218:11125–11137MathSciNetMATHGoogle Scholar
  17. 17.
    Pao Y (1989) Adaptive pattern recognition and neural networks. Addison Wesley, BostonMATHGoogle Scholar
  18. 18.
    Perry SW, Guan L (2000) Weight assignment for adaptive image restoration by neural networks. IEEE Trans Neural Netw 11:156–170CrossRefGoogle Scholar
  19. 19.
    Poisson noise (2016) (Online). Available: Accessed 25 Jun 2016
  20. 20.
    Rao RV (2016) Jaya : A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 719–34:2016Google Scholar
  21. 21.
    Rao RV, Waghmare GG (2016) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49:1–24Google Scholar
  22. 22.
    Rao RV, More KC, Taler J, Ocłoń J (2016) Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 103:572–582CrossRefGoogle Scholar
  23. 23.
    Saadi S, Guessoum A, Bettayeb M (2013) ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess Microsyst 37:52–64CrossRefGoogle Scholar
  24. 24.
    Sicuranza GL, Carini A (2011) A generalized FLANN filter for nonlinear active noise control. IEEE Trans Audio Speech Lang Process 19:2412–2417CrossRefGoogle Scholar
  25. 25.
    Sivakumar K (1993) Image Restoration Using a Multilayer Perceptron with a Multilevel Sigmoidal Function. IEEE Trans of Signal Process 41:2018–2022CrossRefMATHGoogle Scholar
  26. 26.
    Suzuki K, Horiba I, Sugie N (2003) Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans Pattern Anal Mach Intell 25:1582–1596CrossRefGoogle Scholar
  27. 27.
    Wang A, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13:600–612CrossRefGoogle Scholar
  28. 28.
    Wang H, Lv Y, Chen H, Li Y, Zhang Y, Lu Z (2016) Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimed Tools Appl.
  29. 29.
    Wen H, Wen J (2013) Image Denoising and Restoration Using Pulse Coupled Neural Networks. 6th Int. Congr. Image Signal Process. 2013Google Scholar
  30. 30.
    Zeng W, Lu X, Tan X (2013) A local structural adaptive partial differential equation for image denoising. Multimed Tools Appl 74:743–757CrossRefGoogle Scholar
  31. 31.
    Zhang D, Mabu S, Hirasawa K (2010) Noise reduction using genetic algorithm based pcnn method. IEEE conf. Systems Man and Cybernetics (SMC). 2010Google Scholar
  32. 32.
    Zhao H, Zhang J (2008) Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization. Signal Process 88:1946–1957CrossRefMATHGoogle Scholar
  33. 33.
    Zhao H, Zhang J (2010) Pipelined Chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter. IEEE Trans Syst Man, Cybern Part B Cybern 40:162–172CrossRefGoogle Scholar
  34. 34.
    Zhou YT, Chellappa R, Vaid A, Jenkins BK (1988) Image restoration using a neural network. IEEE Trans Acoust 36:1141–1151CrossRefMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Birla Institute of TechnologyRanchiIndia

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