A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Ferzan Katırcıoğlu; Zafer Cingiz

doi:10.31202/ecjse.733519

A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Abstract

Image enhancement is a necessary and indispensable technique for increasing the quality of digital images. The main task is to generate a new intensity value for each pixel in the image using a transformation function after the input image receives the intensity value of each pixel. The proposed transfer function in this study is called the Regional Similarity Transfer Function (RSTF) that considers the density distribution similarity between adjoining pixels. Dragonfly Algorithm (DA) intuitive optimization technique, which is preferred in engineering applications, has been used to optimize the parameter values of the proposed transfer function. Image quality evaluation is performed with six criteria between the improved and original images. Our experimental results show that the intensity distribution between adjoining pixels show an increase in contrast and brightness over the similarity degree. Excessive brightness, blur, and deterioration in the images is resolved with the proposed method.

Keywords

References

Referans 1 Schalkoff, R. J., Digital image processing and computer vision, New York: Wiley Vol. 286 (1989).
Referans 2 Gonzalez, R. C.; Woods, R. E., Digital image processing. (2012)‎.
Referans 3 Russ, J. C., The image processing handbook, CRC press. (2016) ‎.
Referans 4 Kim, Y. T., Contrast enhancement using brightness preserving bi-histogram equalization, IEEE ‎transactions on Consumer Electronics, 1997, 43(1): 1-8.
Referans 5 Wang, Q.; Ward, R. K., Fast image/video contrast enhancement based on weighted ‎thresholded histogram equalization, IEEE transactions on Consumer Electronics, 2007, 53(2): ‎.
Referans 6 Chen, S. D.; Ramli, A. R., Contrast enhancement using recursive mean-separate histogram ‎equalization for scalable brightness preservation, IEEE Transactions on consumer Electronics, 2003, 49(4): 1301-1309.
Referans 7 Sim, K. S.; Tso, C. P.; Tan, Y. Y., Recursive sub-image histogram equalization applied to gray ‎scale images, Pattern Recognition Letters, 2007, 28(10): 1209-1221.‎
Referans 8 Tanaka, G.; Suetake, N.; Uchino, E., Image enhancement based on multiple ‎parametric sigmoid functions, In Intelligent Signal Processing and Communication Systems, ‎ISPACS 2007 IEEE, 2007, 108-111‎.

Referans 9 Kannan, P.; Deepa, S.; Ramakrishnan, R., Contrast enhancement of sports images ‎using modified sigmoid mapping function, In Communication Control and Computing Technologies ‎‎(ICCCCT), 2010 IEEE International Conference, 2010, 651-656.
Referans 10 Verma, H. K.; Pal, S., Modified Sigmoid Function Based Gray Scale Image Contrast ‎Enhancement Using Particle Swarm Optimization, Journal of The Institution of Engineers (India): Series ‎B, 2016, 97(2): 243-251.
Referans 11 Munteanu, C.; Lazarescu, V., Evolutionary contrast stretching and detail enhancement of ‎satellite images, Proceedings of MENDEL’99, 1999, 94-99.
Referans 12 Saitoh, F., Image contrast enhancement using genetic algorithm, In Systems, Man, and ‎Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference, 1999, 4: 899-904.
Referans 13 Munteanu, C.; Rosa, A., Towards automatic image enhancement using genetic algorithms, In ‎Evolutionary Computation, 2000. Proceedings of the 2000 Congress, 2000, 2: 1535-1542‎.
Referans 14 Gorai, A.; Ghosh, A., Gray-level image enhancement by particle swarm ‎optimization, In Nature & Biologically Inspired Computing, NaBIC, 2009, ‎‎72-77‎. Referans 15 Zhao, W., Adaptive image enhancement based on gravitational search algorithm, Procedia ‎Engineering, 2011, 15: 3288-3292. ‎
Referans 16 Agrawal, S.; Panda, R., An efficient algorithm for gray level image enhancement ‎using cuckoo search, In International Conference on Swarm, Evolutionary, and Memetic Computing, Springer, Berlin, Heidelberg‎, 2012, 82-89.
Referans 17 Sarangi, P. P.; Mishra, B. S. P.; Majhi, B.; Dehuri, S., Gray-level image enhancement ‎using differential evolution optimization algorithm, In Signal Processing and Integrated Networks ‎‎(SPIN), 2014, 95-100.
Referans 18 Murali, K.; Jayabarathi, T., Automated image enhancement using Grey-wolf optimizer algorithm, J Multidiscip Sci Technol, 2016, 7: 77-84.
Referans 19 Draa, A.; Bouaziz, A., An artificial bee colony algorithm for image contrast enhancement, ‎Swarm and Evolutionary computation, 2014, 16: 69-84.
Referans 20 Ozturk, S.; Ozturk, N., Yapay arı koloni algoritması kullanarak görüntü iyileştirme yönteminin geliştirilmesi, Gazi University Fen Bilimleri Dergisi Part C: Tasarım ve ‎Teknoloji, 2016, 4(4): 173-183.
Referans 21 Demirci, R.; Katircioglu, F., Segmentation of color ‎images based on relation ‎matrix, Signal ‎Processing and ‎Communications Applications, SIU 2007, 2007.
Referans 22 Katircioglu, F., Segmentation of color images based ‎on ‎relation matrix and edge detection, Master of ‎Science, ‎‎Dept. Electrical Education, Duzce Universtiy, Duzce, ‎‎Turkey, 2007‎.
Referans 23 Ye, Z.; Wang, M.; Hu, Z.; Liu, W., An adaptive image enhancement technique by combining ‎cuckoo search and particle swarm optimization algorithm, Computational intelligence and ‎neuroscience, 2015, 13‎.
Referans 24 Reynolds, C. W., Flocks, herds and schools: A distributed behavioral model, In ACM ‎Siggraph computer graphics, 1987, 21(4): 25-34‎.
Referans 25 Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-‎objective, discrete, and multi-objective problems, Neural Computing and Applications, 2016, 27(4): 1053-‎‎1073.
Referans 26 Kwok, N. M.; Ha, Q. P.; Liu, D.; Fang, G., Contrast enhancement and intensity preservation for ‎gray-level images using multiobjective particle swarm optimization, IEEE Transactions on Automation ‎Science and Engineering, 2009, 6(1): 145-155.
Referans 27 Dos Santos Coelho, L.; Sauer, J. G.; Rudek, M., Differential evolution optimization combined ‎with chaotic sequences for image contrast enhancement, Chaos, Solitons & Fractals, 2009, 42(1): 522-529‎.
Referans 28 Rashedi, E.; Nezamabadi-Pour, H.; Saryazdi, S., GSA: a gravitational search algorithm, ‎Information sciences, 2009, 179(13): 2232-2248.
Referans 29 Lee, J. S., Digital image enhancement and noise filtering by use of local statistics, IEEE ‎transactions on pattern analysis and machine intelligence, 1980, 2: 165-168. ‎ Referans 30 Prashanth, H. S.; Shashidhara, H. L.; KN, B. M., Image scaling comparison using ‎universal image quality index, In Advances in Computing, Control, & Telecommunication Technologies, ‎‎ACT'09, 2009, 859-86. Referans 31 Nakhmani, A.; Tannenbaum, A., A new distance measure based on generalized image ‎normalized cross-correlation for robust video tracking and image recognition, Pattern recognition ‎letters, 2013, 34(3): 315-321.
Referans 32 Rajkumar, S.; Malathi, G. A comparative analysis on image quality assessment for real time ‎satellite images, Indian Journal of Science and Technology, 2016, 9(34)‎.
Referans 33 Wang, Z.; Bovik, A. C.; Sheikh, H.R.; Simoncelli, E. P., Image quality assessment: from error ‎visibility to structural similarity, IEEE transactions on image processing, 2004, 13(4): 600-612. ‎

Details

Primary Language

Turkish

Subjects

Engineering

Journal Section

Research Article

Authors

Ferzan Katırcıoğlu ^*
0000-0001-5463-3792
Türkiye

Zafer Cingiz
0000-0003-3796-755X
Türkiye

Publication Date

September 30, 2020

Submission Date

May 7, 2020

Acceptance Date

August 17, 2020

Published in Issue

Year 2020 Volume: 7 Number: 3

DOI

https://doi.org/10.31202/ecjse.733519

IZ

https://izlik.org/JA84KJ84BG

Cite

RIS / Bibtex

APA

Katırcıoğlu, F., & Cingiz, Z. (2020). A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm. El-Cezeri, 7(3), 1201-1219. https://doi.org/10.31202/ecjse.733519

AMA

1.Katırcıoğlu F, Cingiz Z. A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm. El-Cezeri Journal of Science and Engineering. 2020;7(3):1201-1219. doi:10.31202/ecjse.733519

Chicago

Katırcıoğlu, Ferzan, and Zafer Cingiz. 2020. “A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm”. El-Cezeri 7 (3): 1201-19. https://doi.org/10.31202/ecjse.733519.

EndNote

Katırcıoğlu F, Cingiz Z (September 1, 2020) A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm. El-Cezeri 7 3 1201–1219.

IEEE

[1]F. Katırcıoğlu and Z. Cingiz, “A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm”, El-Cezeri Journal of Science and Engineering, vol. 7, no. 3, pp. 1201–1219, Sept. 2020, doi: 10.31202/ecjse.733519.

ISNAD

Katırcıoğlu, Ferzan - Cingiz, Zafer. “A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm”. El-Cezeri 7/3 (September 1, 2020): 1201-1219. https://doi.org/10.31202/ecjse.733519.

JAMA

1.Katırcıoğlu F, Cingiz Z. A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm. El-Cezeri Journal of Science and Engineering. 2020;7:1201–1219.

MLA

Katırcıoğlu, Ferzan, and Zafer Cingiz. “A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm”. El-Cezeri, vol. 7, no. 3, Sept. 2020, pp. 1201-19, doi:10.31202/ecjse.733519.

Vancouver

1.Ferzan Katırcıoğlu, Zafer Cingiz. A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm. El-Cezeri Journal of Science and Engineering. 2020 Sep. 1;7(3):1201-19. doi:10.31202/ecjse.733519

A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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