Skip to main content
SpringerLink
Log in

Identification for the Low-Contrast Image Signal with Regularized Variational Term and Dynamical Saturating Nonlinearity

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

In recent years, image processing based on stochastic resonance (SR) has received more and more attention. In this paper, a new model combining dynamical saturating nonlinearity with regularized variational term for enhancement of low contrast image is proposed. The regularized variational term can be setting to total variation (TV), second order total generalized variation (TGV) and non-local means (NLM) in order to gradually suppress noise in the process of solving the model. In addition, the new model is tested on a mass of gray-scale images from standard test image and low contrast indoor color images from Low-Light dataset (LOL). By comparing the new model and other traditional image enhancement models, the results demonstrate the enhanced image not only obtain good perceptual quality but also get more excellent value of evaluation index compared with some previous methods.

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

Similar content being viewed by others

References

  1. Singh R P and Dixit M, Histogram equalization: A strong technique for image enhancement, International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8(8): 345–352.

    Article  Google Scholar 

  2. Pizer S M, Johnston R E, Ericksen J P, et al., Contrast-limited adaptive histogram equalization: Speed and effectiveness, Proceedings of the First Conference on Visualization in Biomedical Computing, 1990.

  3. Xu G, Su J, Pan H D, et al., An image enhancement method based on gamma correction, 2009 Second International Symposium on Computational Intelligence and Design, 2009, 1: 60–63.

    Google Scholar 

  4. Choi D H, Jang I H, Kim M H, et al., Color image enhancement using single-scale retinex based on an improved image formation model, 2008 16th European Signal Processing Conference, Switzerland, 2008.

  5. Rahman Z, Jobson D J, and Woodell G A, Multi-scale retinex for color image enhancement, Proceedings of 3rd IEEE International Conference on Image Processing, 1996, 3: 1003–1006.

    Article  Google Scholar 

  6. Benzi R, Sutera A, and Vulpiani A, The mechanism of stochastic resonance, Journal of Physics A: Mathematical and General, 1981, 14(11): L453.

    Article  MathSciNet  Google Scholar 

  7. Benzi R, Parisi G, Sutera A, et al., Stochastic resonance in climatic change, Tellus, 1982, 34(1): 10–16.

    Article  MATH  Google Scholar 

  8. Hoult D I and Richards R E, The signal-to-noise ratio of the nuclear magnetic resonance experiment, Journal of Magnetic Resonance, 1976, 24(1): 71–85.

    Google Scholar 

  9. Firbank M J, Coulthard A, Harrison R M, et al., A comparison of two methods for measuring the signal to noise ratio on MR images, Physics in Medicine & Biology, 1999, 44(12): N261.

    Article  Google Scholar 

  10. Chouhan R, Jha R K, and Biswas P K, Enhancement of dark and low-contrast images using dynamic stochastic resonance, IET Image Processing, 2013, 7(2): 174–184.

    Article  MathSciNet  Google Scholar 

  11. Zhang J, Liu H, and Wei Z, Regularized variational dynamic stochastic resonance method for enhancement of dark and low-contrast image, Computers & Mathematics with Applications, 2018, 6(4): 774–787.

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu H and Zhang J, Filtering combined dynamic stochastic resonance for enhancement of dark and low-contrast images, 2017 International Conference on Progress in Informatics and Computing (PIC), Nanjing, 2017.

  13. Chouhan R, Jha R K, and Biswas P K, Image denoising using dynamic stochastic resonance in wavelet domain, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi, 2012.

  14. Chouhan R, Kumar C P, Kumar R, et al., Contrast enhancement of dark images using stochastic resonance in wavelet domain, International Journal of Machine Learning and Computing, 2012, 2(5): 711–715.

    Article  Google Scholar 

  15. Rallabandi V P S, Enhancement of ultrasound images using stochastic resonance-based wavelet transform, Computerized Medical Imaging and Graphics, 2008, 32(4): 316–320.

    Article  Google Scholar 

  16. Rallabandi V P S and Roy P K, Magnetic resonance image enhancement using stochastic resonance in Fourier domain, Magnetic Resonance Imaging, 2010, 28(9): 1361–1373.

    Article  Google Scholar 

  17. Jha R K and Chouhan R, Dynamic stochastic resonance-based grayscale logo extraction in hybrid SVD-DCT domain, Journal of the Franklin Institute, 2014, 351(5): 2938–2965.

    Article  MathSciNet  MATH  Google Scholar 

  18. Gupta N, Jha R K, and Mohanty S K, Enhancement of dark images using dynamic stochastic resonance in combined DWT and DCT domain, 2014 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, 2014.

  19. Ryu C, Kong S G, and Kim H, Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance, Pattern Recognition Letters, 2011, 32(2): 107–113.

    Article  Google Scholar 

  20. Jha R K, Biswas P K, and Shrivastava S, Logo extraction using dynamic stochastic resonance, Signal, Image and Video Processing, 2013, 7(1): 119–128.

    Article  Google Scholar 

  21. Zhao J, Ma Y, Pan Z, et al., Research on image signal identification based on adaptive array stochastic resonance, Journal of Systems Science & Complexity, 2021, 35(1): 1–15.

    Google Scholar 

  22. Zhang H, Yu J, Ma Y, et al., Image restoration based on stochastic resonance in a parallel array of Fitzhugh-Nagumo Neuron, Complexity, 2020, 2020: 1–9.

    Google Scholar 

  23. Rudin L I, Osher S, and Fatemi E, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 1992, 60(1–4): 259–268.

    Article  MathSciNet  MATH  Google Scholar 

  24. Chambolle A, Caselles V, Cremers D, et al., An introduction to total variation for image analysis, Theoretical Foundations and Numerical Methods for Sparse Recovery, 2010, 9(263–340): 227.

    MathSciNet  MATH  Google Scholar 

  25. Buades A, Coll B, and Morel J M, Non-local means denoising, Image Processing on Line, 2011, 1: 208–212.

    Article  MATH  Google Scholar 

  26. Gao Y M, Liu F, and Yang X P, Total generalized variation restoration with non-quadratic fidelity, Multidimensional Systems & Signal Processing, 2018, 29: 1459–1484.

    Article  MATH  Google Scholar 

  27. Mei J J, Huang T Z, Wang S, et al., Second order total generalized variation for speckle reduction in ultrasound images, Journal of the Franklin Institute, 2018, 355(1): 574–595.

    Article  MathSciNet  MATH  Google Scholar 

  28. Song Q and Huang Z, Identification of errors-in-variables systems with general nonlinear output observations and with arma observation noises, Journal of Systems Science & Complexity, 2020, 33(1): 1–14.

    Article  MathSciNet  MATH  Google Scholar 

  29. Lu T, Lan W, and Li Z, Transient performance improvement in tracking control for a class of nonlinear systems with input saturation, Journal of Systems Science & Complexity, 2018, 31(1): 200–214.

    Article  MathSciNet  MATH  Google Scholar 

  30. Belfeki M, Adaptive output feedback regulation for a class of uncertain feedforward time-delay nonlinear systems, Journal of Systems Science & Complexity, 2020, 33(3): 604–621.

    Article  MathSciNet  MATH  Google Scholar 

  31. Long Y, Liu S, Xie L, et al., Stochastic channel allocation for nonlinear systems with markovian packet dropout, Journal of Systems Science & Complexity, 2018, 31(1): 22–37.

    Article  MathSciNet  MATH  Google Scholar 

  32. Ma Y and Duan F, Comparison of stochastic resonance in static and dynamical nonlinearities, Physics Letters A, 2014, 378(36): 2651–2656.

    Article  MATH  Google Scholar 

  33. Wang Z, Shen X, and Zhu Y, Posterior cramr-rao bounds for nonlinear dynamic system with colored noises, Journal of Systems Science & Complexity, 2019, 32(6): 1526–1543.

    Article  MathSciNet  MATH  Google Scholar 

  34. Boyd S, Parikh N, and Chu E, Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine Learning, 2011, 3(1): 1–22.

    Article  MATH  Google Scholar 

  35. Chambolle A, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, 2004, 20(1): 89–97.

    MathSciNet  MATH  Google Scholar 

  36. Bredies K, Kunisch K, and Pock T, Total generalized variation, SIAM Journal on Imaging Sciences, 2010, 3(3): 492–526.

    Article  MathSciNet  MATH  Google Scholar 

  37. Singh P and Pradhan G, Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering, Australasian Physical & Engineering Sciences in Medicine, 2018, 41(4): 891–904.

    Article  Google Scholar 

  38. Jha R K, Chouhan R, Biswas P K, et al., Internal noise-induced contrast enhancement of dark images, 2012 19th IEEE International Conference on Image Processing, Orlando, 2012.

  39. Singh S and Bovis K, An evaluation of contrast enhancement techniques for mammographic breast masses, IEEE Transactions on Information Technology in Biomedicine, 2005, 9(1): 109–119.

    Article  Google Scholar 

  40. Wang Z, Sheikh H R, and Bovik A C, No-reference perceptual quality assessment of JPEG compressed images, Proceedings of International Conference on Image Processing, 2002, 1: I–I.

    Google Scholar 

  41. Otsu N, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66.

    Article  MathSciNet  Google Scholar 

  42. Yu F, Sun W, Li J, et al., An improved Otsu method for oil spill detection from SAR images, Oceanologia, 2017, 59(3): 311–317.

    Article  Google Scholar 

  43. Mukherjee J and Mitra S K, Enhancement of color images by scaling the DCT coefficients, IEEE Transactions on Image processing, 2008, 17(10): 1783–1794.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science & Technology, Qingdao University, Qingdao, 266071, China

    Ning Zhang, Yumei Ma, Zhenkuan Pan, Baoxiang Huang & Dongcheng Wang

Authors
  1. Ning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yumei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenkuan Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baoxiang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongcheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yumei Ma.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 61501276, 61772294 and 61973179, the China Postdoctoral Science Foundation under Grant No. 2016M592139, and the Qingdao Postdoctoral Applied Research Project under Grant No. 2015120.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, N., Ma, Y., Pan, Z. et al. Identification for the Low-Contrast Image Signal with Regularized Variational Term and Dynamical Saturating Nonlinearity. J Syst Sci Complex 36, 1089–1102 (2023). https://doi.org/10.1007/s11424-023-1270-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-023-1270-5

Keywords

Search

Navigation