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An image contrast enhancement algorithm for grayscale images using particle swarm optimization

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Abstract

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.

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References

  1. Agaian SS, Lentz KP, Grigoryan AM (2000) A new measure of image enhancement. In: IASTED International Conference on Signal Processing and Communication, Marbella, Spain, pp 19–22

  2. Aghagolzadeh S, Ersoy O (1992) Transform image enhancement. Opt Eng 31(3):614–626

    Article  Google Scholar 

  3. Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T, Al-Rodhaan M (2015) An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP Journal on Advances in Signal Processing 32:1–12

    Google Scholar 

  4. Amiri SA, Hassanpour H (2012) A preprocessing approach for image analysis using gamma correction. Int J Comput Appl 38(12):38–46

    Google Scholar 

  5. Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 18(9):1921–1935

    Article  MathSciNet  MATH  Google Scholar 

  6. Bechara B, McMahan CA, Moore WS, Noujeim M, Geha H, Teixeira FB (2012) Contrast-to-noise ratio difference in small field of view cone beam computed tomography machines. J Oral Sci 54:227–232

    Article  Google Scholar 

  7. Bhattacharyya S, Dutta P (2015) Handbook of research on swarm intelligence in engineering (advances in computational intelligence and robotics). IGI global, Hershey

    Google Scholar 

  8. Caselles V, Lisani J, Morel J, Sapiro G (1998) Shape preserving local histogram modification. IEEE Trans Image Process 8(2):220–230

    Article  Google Scholar 

  9. Chen S, Ramli A (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309

    Article  Google Scholar 

  10. Chen S-D, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319

    Article  Google Scholar 

  11. Chen S, Ramli A (2004) Preserving brightness in histogram equalization based contrast enhancement techniques. Digital Signal Process 14(5):413–428

    Article  Google Scholar 

  12. Chen S-D, Ramli AR (2004) Preserving brightness in histogram equalization based contrast enhancement techniques. Digital Signal Process 14:413–428

    Article  Google Scholar 

  13. Cheng HD, Xue M, Shi XJ (2003) Contrast enhancement based on a novel homogeneity measurement. Pattern Recogn 36:2687–2697

    Article  Google Scholar 

  14. Chiu YS, Cheng FC, Huang SC (2011) Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, USA, pp 2946–2950

  15. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  16. Coltuc D, Bolon P, Chassery J (2006) Exact histogram specification. IEEE Trans Image Process 15(5):1143–1151

    Article  Google Scholar 

  17. Coltuc D, Bolon P, Chassery J (2006) Exact histogram specification. IEEE Trans Image Process 15(5):1143–1151

    Article  Google Scholar 

  18. Gonzalez RC, Woods RE (2007) Digital Image Processing, third edition, Pearson Education, London

  19. Gorai A, Ghosh A (2009) Gray-level image enhancement by particle swarm optimization. In: Proceedings of Nature and Biologically Inspired Computing, 2009: NaBIC 2009. https://doi.org/10.1109/NABIC.2009.5393603

  20. Hassanpour H, Amiri SA (2011) Image quality enhancement using pixel-wise gamma correction via SVM classifier. IJE Trans B: Applications 24(4):301

    Article  Google Scholar 

  21. Hu Y, Zhao CX, Wang HN (2008) Directional analysis of texture images using gray level co-occurrence matrix. In: PACIIA ‘08: Pacific-Asia Workshop on Computational Intelligence and Industrial Application. IEEE, Wuhan, pp 277–281

  22. Huang S-C, Cheng F-C, Chiu Y-S (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Tranaction on Image Processing 22(3):1032–1041

    Article  MathSciNet  MATH  Google Scholar 

  23. Jaya VL, Gopikakumari R (2013) IEM: a new image enhancement metric for contrast and sharpness measurements. Int J Comput Appl (0975 8887) 79(9):1–9

    Google Scholar 

  24. Kanmani M, Narsimhan V (2016) Swarm intelligence based optimisation in thermal image fusion using dual tree discrete wavelet transform. Quantitative Infrared Thermography Journal, Taylor and Francis 14(1):24–43

    Google Scholar 

  25. Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equation. IEEE Trans Consum Electron 43(1):1–8

    Article  MathSciNet  Google Scholar 

  26. Navas KA, Gayathri DKG, Athulya MS, Vasudev A (2011) MWPSNR: a new image fidelity metric. In: 2011 I.E. Recent Advances in Intelligent Computational Systems (RAICS). IEEE, Trivandrum, pp 627–632

  27. Ooi CH, Mat Isa NA (2010) Adaptive contrast enhancement methods with brightness preserving. IEEE Trans Consum Electron 56(4):2543–2551

    Article  Google Scholar 

  28. Ooi CH, Mat Isa NA (2010) Quadrants dynamic histogram equalization for contrast enhancement. IEEE Trans Consum Electron 56(4):2552–2559

    Article  Google Scholar 

  29. Pizer S, Amburn E, Austin J, Cromartie R, Geselowitz A, Greer T, Romeny B, Zimmerman J, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  30. Qinqing G, Guangping Z, Dexin C, Ketai H (2011) Image enhancement technique based on improved PSO algorithm. In: Proceedings of Industrial Electronics and Applications (ICIEA), pp 234–238. https://doi.org/10.1109/ICIEA.2011.5975586

  31. Rani S, Kumar M (2014) Contrast enhancement using improved adaptive gamma correction with weighting distribution technique. Int J Comput Appl 101(11):47–53

    Google Scholar 

  32. Rao SS (2013) Engineering optimization theory and practice, 3rd edn. Wiley, Hoboken

  33. Shanmugavadivu P, Balasubramanian K (2014) Thresholded and optimized histogram equalization for contrast enhancement of images. Comput Electr Eng 40:757–768

    Article  Google Scholar 

  34. Sheet D, Garud H, Suveer A, Mahadevappa M, Chatterjee J (2010) Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans Consum Electron 56(4):2475–2480

    Article  Google Scholar 

  35. Sim KS, Tso CP, Tan YY (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28:1209–1221

    Article  Google Scholar 

  36. Stark J (2000) Adaptive contrast enhancement using generalization of histogram equalization. IEEE Trans Image Process 9(5):889–906

    Article  Google Scholar 

  37. Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75

    Article  Google Scholar 

  38. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Academic Press, Cambridge

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SSN College of Engineering, Chennai, India

    Madheswari Kanmani

  2. Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India

    Venkateswaran Narsimhan

Authors
  1. Madheswari Kanmani
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  2. Venkateswaran Narsimhan
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Correspondence to Madheswari Kanmani.

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Kanmani, M., Narsimhan, V. An image contrast enhancement algorithm for grayscale images using particle swarm optimization. Multimed Tools Appl 77, 23371–23387 (2018). https://doi.org/10.1007/s11042-018-5650-0

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  • DOI: https://doi.org/10.1007/s11042-018-5650-0

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