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Enhancement of image contrast using Selfish Herd Optimizer

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Abstract

Contrast enhancement is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO(direct)) and transformation function based optimization (SHO(transformation)). Moreover, an ablation study is performed to select the most appropriate parameters which can be used in fitness measure for this optimization problem. On experimenting over the popular Kodak image dataset, it has been observed that the proposed methods outperform many existing methods published recently. Further comparisons indicate that the direct approach performs better than its transformation counterpart. This paper further investigates the robustness of SHO(direct) approach by applying it to enhance the degraded document images of H-DIBCO 2018.

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References

  1. Agrawal S, Panda R (2012, December) An efficient algorithm for gray level image enhancement using cuckoo search. In: International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Berlin, Heidelberg, pp 82–89

    Chapter  Google Scholar 

  2. Akay B, Karaboga D (2015) A survey on the applications of artificial bee colony in signal, image, and video processing. Signal Process Image Commun 9(4):967–990

    Google Scholar 

  3. Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490. https://doi.org/10.1109/tpami.2005.173

    Article  Google Scholar 

  4. Bhardwaj S, Mittal A (2012) A survey on various edge detector techniques. Procedia Technology 4, 220–226. 2nd International Conference on Computer, Communication, Control and Information Technology (C3IT-2012) on February 25–26, 2012. https://doi.org/10.1016/j.protcy.2012.05.033. http://www.sciencedirect.com/science/article/pii/S221201731200312X

  5. Chen J, Yu W, Tian J, Chen L, Zhou Z (2018) Image contrast enhancement using an artificial bee colony algorithm. Swarm Evol Comput 38:287–294. https://doi.org/10.1016/j.swevo.2017.09.002

    Article  Google Scholar 

  6. Daniel E, Anitha J (2016) Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm. Comput Biol Med 71:149–155

    Article  Google Scholar 

  7. Deborah H, Arymurthy AM (2010) Image enhancement and image restoration for old document image using genetic algorithm. In 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies. IEEE, pp 108–112. https://doi.org/10.1109/act.2010.24

  8. Draa A, Bouziz A (2014) An artificial bee colony algorithm for image contrast enhancement. Swarm Evol Comput 16. https://doi.org/10.1016/j.swevo.2014.01.003

  9. Franzen R (1999) Kodak lossless true color image suite. http://r0k.us/graphics/kodak/

  10. Ghosh M, Bera SK, Guha R, Sarkar R (2019) Contrast enhancement of degraded document image using partitioning based genetic algorithm

  11. Ghosh M, Guha R, Alam I, Lohariwal P, Jalan D, Sarkar R (2019) Binary genetic swarm optimization: A combination of GA and PSO for feature selection. J Intell Syst 29(1):1598–1610. https://doi.org/10.1515/jisys-2019-0062

    Article  Google Scholar 

  12. Ghosh M, Guha R, Sarkar R, Abraham A (2019) A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput Applic. https://doi.org/10.1007/s00521-019-04171-3

    Article  Google Scholar 

  13. Gong T, Fan T, Pei L, Cai Z (2017) Magnetic resonance imaging-clonal selection algorithm: An intelligent adaptive enhancement of brain image with an improved immune algorithm. Eng Appl Artif Intell 62:405–411

    Article  Google Scholar 

  14. Gu K, Zhai G, Lin W, Liu M (2015) The analysis of image contrast: From quality assessment to automatic enhancement. IEEE Trans Cybern 46(1):284–297

    Article  Google Scholar 

  15. Guha R, Ghosh M, Kapri S, Shaw S, Mutsuddi S, Bhateja V, Sarkar R (2019) Deluge based genetic algorithm for feature selection. Evol Intell. https://doi.org/10.1007/s12065-019-00218-5

    Article  Google Scholar 

  16. Hashemi S, Kiani S, Noroozi N, Ebrahimi Moghaddam M (2010) An image enhancement method based on genetic algorithm. Pattern Recogn Lett 31:1816–1824

    Article  Google Scholar 

  17. Joshi P, Prakash S (2015) An efficient technique for image contrast enhancement using artificial bee colony. IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015) 1–6

  18. Kanmani M, Narsimhan V (2018) An image contrast enhancement algorithm for grayscale images using particle swarm optimization. Multimed Tools Appl 77(18):23371–23387

    Article  Google Scholar 

  19. Kim H-J, Lee J-M, Lee J-A, Oh S-G, Kim W-Y (2006) Contrast enhancement using adaptively modified histogram equalization. Advances in Image and Video Technology (Berlin, Heidelberg. Springer, Berlin Heidelberg, pp 1150–1158

    Chapter  Google Scholar 

  20. Kittler J (1983) On the accuracy of the sobel edge detector. Image Vis Comput 1(1):37–42. https://doi.org/10.1016/0262-8856(83)90006-9

    Article  Google Scholar 

  21. Liao X, Li K, Zhu X, Liu KJR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Sign Proces 14(5):955–968. https://doi.org/10.1109/jstsp.2020.3002391

    Article  Google Scholar 

  22. Liao X, Yin J, Chen M, Qin Z (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE Trans Dependable Secure Comput 1–1. https://doi.org/10.1109/tdsc.2020.3004708

  23. Ling Z, Wang Y, Shen H, Liang Y, Lu X (2015) Adaptive extended piecewise histogram equalisation for dark image enhancement. IET Image Proc 9(11):1012–1019. https://doi.org/10.1049/iet-ipr.2014.0580

    Article  Google Scholar 

  24. Lu H, Kot A, Shi Y (2004) Distance-reciprocal distortion measure for binary document images. IEEE Signal Process Lett 11(2):228–231. https://doi.org/10.1109/lsp.2003.821748

    Article  Google Scholar 

  25. Poddar S, Tewary S, Sharma D, Karar V, Ghosh A, Pal SK (2013) Non-parametric modified histogram equalisation for contrast enhancement. IET Image Process 7(7):641–652. https://doi.org/10.1049/iet-ipr.2012.0507

    Article  Google Scholar 

  26. Pratikakis I, Zagori K, Kaddas P, Gatos B (2018) ICFHR 2018 competition on handwritten document image binarization (h-DIBCO 2018). In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, pp 489–493. https://doi.org/10.1109/icfhr-2018.2018.00091

  27. Pratikakis I, Zagoris K, Karagiannis X, Tsochatzidis L (2019) ICDAR 2019 competition on document image binarization (DIBCO 2019). In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp 1547–1556. https://doi.org/10.1109/ICDAR.2019.00249

  28. Qinqing G, Dexin C, Guangping Z, Ketai H (2011) Image enhancement technique based on improved PSO algorithm. In 2011 6th IEEE Conference on Industrial Electronics and Applications. pp 234–238. https://doi.org/10.1109/ICIEA.2011.5975586

  29. Russo F (2004) Piecewise linear model-based image enhancement. EURASIP J Adv Signal Process 2004:12. https://doi.org/10.1155/s1110865704404041

    Article  Google Scholar 

  30. Santhi K, Banu RW (2015) Adaptive contrast enhancement using modified histogram equalization. Optik - International Journal for Light and Electron Optics 126(19):1809–1814. https://doi.org/10.1016/j.ijleo.2015.05.023

    Article  Google Scholar 

  31. Sheikh H, Bovik A (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444. https://doi.org/10.1109/tip.2005.859378

    Article  Google Scholar 

  32. Singh M, Verma A, Sharma N (2017) Bat optimization based neuron model of stochastic resonance for the enhancement of mr images. Bioprocess Biosyst Eng 37(1):124–134

    Google Scholar 

  33. Srihari S, Shetty S, Chen S, Srinivasan H, Huang C, Agam G, Frieder O (2006) Document image retrieval using signatures as queries. In: Second International Conference on Document Image Analysis for Libraries (DIAL’06). IEEE, pp 6–203. https://doi.org/10.1109/dial.2006.17

  34. Tao L, Zhu C, Song J, Lu T, Jia H, Xie X (2017) Low-light image enhancement using CNN and bright channel prior. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, pp 3215–3219

  35. Tian J, Chen L (2012) Image noise estimation using a variation-adaptive evolutionary approach. IEEE Signal Processing Lett 19(7):395–398

    Article  Google Scholar 

  36. Tian J, Chen L, Ma L, Yu W (2011) Multi-focus image fusion using a bilateral gradient-based sharpness criterion. Opt Commun 284(1):80–87

    Article  Google Scholar 

  37. Tubbs J (1987) A note on parametric image enhancement. Pattern Recogn 20(6):617–621. https://doi.org/10.1016/0031-3203(87)90031-8

    Article  Google Scholar 

  38. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/tip.2003.819861

    Article  Google Scholar 

  39. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans on Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

  40. Westphal F, Lavesson N, Grahn H (2018) Document image binarization using recurrent neural networks. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, pp 263–268. https://doi.org/10.1109/das.2018.71

  41. Winkler S, Mohandas P (2008) The evolution of video quality measurement: From PSNR to hybrid metrics. IEEE Trans Broadcast 54(3):660–668. https://doi.org/10.1109/tbc.2008.2000733

    Article  Google Scholar 

  42. Wong WJ, Lai S-H (2020) Multi-task CNN for restoring corrupted fingerprint images. Pattern Recogn 101:107203

    Article  Google Scholar 

  43. Xue Y, Jiang J, Zhao B, Ma T (2018) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 22(9):2935–2952

    Article  Google Scholar 

  44. Ye Z, Wang M, Hu Z, Liu W (2015) An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm. Comput Intell Neurosci 2015:1–12. https://doi.org/10.1155/2015/825398

    Article  Google Scholar 

  45. Yugandhar D, Nayak S (2015) A comparative study of evolutionary based optimization algorithms on image quality enhancement. Int J Appl Eng Res 10(15):35247–35252

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Ritam Guha, Imran Alam, Suman Kumar Bera & Ram Sarkar

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Neeraj Kumar

  3. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Neeraj Kumar

  4. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala (Punjab), India

    Neeraj Kumar

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  1. Ritam Guha
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  5. Ram Sarkar
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Correspondence to Ritam Guha.

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Guha, R., Alam, I., Bera, S.K. et al. Enhancement of image contrast using Selfish Herd Optimizer. Multimed Tools Appl 81, 637–657 (2022). https://doi.org/10.1007/s11042-021-11404-y

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