Abstract
Creating new, effective, and easy-to-implement techniques of image enhancement for real-time use in mobile applications is now extremely relevant. This paper addresses the problem of improving the efficiency of enhancing images by transforming their intensity in automatic mode. The purpose of this work is to improve the efficiency of enhancing the images by using the technique of piecewise linear contrast stretching. To this end, two new approaches to defining the gain factors have been proposed to implement the technique of piecewise linear stretching for the case of an arbitrary finite number of intervals. The first approach is based on the assumption that mean-separated intervals should be stretched to the same size (length) in the processed image. Another proposed approach is based on the analysis of the number and cumulative brightness of elements in the mean-separated intervals. The proposed approaches to defining gain factors ensure the implementation of the procedure of piecewise linear stretching for any selected number of intervals. To demonstrate the capabilities of these approaches, a new technique of recursive mean-separate contrast stretching (RMSCS) was proposed, which is based on the proposed methods of defining gain factors. The RMSCS technique provides a more uniform distribution of the contrast of objects in the image compared to traditional piecewise-linear contrast stretching. The proposed RMSCS technique has a number of advantages over known methods of transforming intensity and can be considered as an alternative to the widely used technique of histogram equalization and its modifications, in particular, based on the sub-histograms equalization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xu, L., Doermann, D.: Computer vision and image processing techniques for mobile application. Center for Automation Research, University of Maryland LAMP-TR-151 (2008)
Pratt, W.K.: Digital Image Processing: PIKS Scientific inside, 4th edn. Pixel Soft Inc., Los Altos (2017)
Gonzalez, R., Woods, R.: Digital Image Processing. 4th edn. Pearson Education, London (2018). ISBN 978-0-13-335672-4
Woods, R.E., Gonzalez, R.C.: Real-time digital image enhancement. Proc. IEEE 69(5), 643–654 (1981). https://doi.org/10.1109/PROC.1981.12031
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, 2nd edn. Gatesmark Publishing, Mexico (2009)
Yaroslavsky, L.: Digital Holography and Digital Image Processing. Springer, New York (2004). https://doi.org/10.1007/978-1-4757-4988-5
Bovik, A.C.: Handbook of Image and Video Processing, 2nd edn. Academic Press, A Harcourt Science and Technology Company, San Diego (2005). ISBN-10:0121197905/ISBN-13:9780121197902
Burger, W., Burge, M.J.: Point Operations. In: Principles of Digital Image Processing. Undergraduate Topics in Computer Science. Springer, London (2009)
Baidoo, E., Kontoh, A.: Implementation of gray level image transformation techniques. Int. J. Mod. Educ. Comput. Sci. 5, 44–53 (2018)
Rao, Y., Chen, L.: A survey of video enhancement techniques. J. Inf. Hiding Multimed. Signal Process. 3(1), 71–99 (2012)
Kotkar, V., Gharde, S.: Review of various image contrast enhancement techniques. Int. J. Innov. Res. Sci. Eng. Technol. 2(7), 2786–2793 (2013). Corpus ID: 52206143
Mokhtar, N., Harun, N., Mashor, M.Y.: Image enhancement techniques using local, global, bright, dark and partial contrast stretching for acute leukemia images. In: Proceedings of the World Congress on Engineering WCE, vol. 1, pp. 807–812, London, UK (2009)
Radha, N., Tech, M.: Comparison of contrast stretching methods of image enhancement techniques for acute leukemia images. Int. J. Eng. Res. Technol. IJERT 1(6), 1–7 (2012). ISSN 2278-0181
Maragatham, G., Roomi, M.: A review of image contrast enhancement methods and technique. Res. J. Appl. Sci. Eng. Technol. 9(5), 309–326 (2015). https://doi.org/10.19026/rjaset.9.1409
Yelmanov, S., Romanyshyn, Y.: Image contrast enhancement in automatic mode by nonlinear stretching. In: Proceedings of 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 104–108. IEEE (2018)
Hummel, R.: Histogram modification techniques. Comput. Graph. Image Process. 4(3), 209–224 (1975). https://doi.org/10.1016/0146-664X(75)90009-X
Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Cons. Electron. 43(1), 1–8 (1997)
Chen, S.D., Ramli, A.: Contrast enhancement using recursive mean separate histogram equalization for scalable brightness preservation. IEEE Trans. Cons. Electron. 49(4), 1301–1309 (2003). https://doi.org/10.1109/TCE.2003.1261233
Kodak. Kodak lossless true color image suite. http://r0k.us/graphics/kodak/
Public-Domain Test Images for Homeworks and Projects. https://homepages.cae.wisc.edu/~ece533/images/arctichare.png
Yelmanov, S., Romanyshyn, Y.: A new approach to measuring perceived contrast for complex images. In: Shakhovska, N., Medykovskyy, M.O. (eds.) CSIT 2018. AISC, vol. 871, pp. 85–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01069-0_7
Michelson, A.A.: Studies in Optics. University of Chicago Press, Chicago (1927)
Nesteruk, V., Sokolova, V.: Questions of the theory of perception of subject images and a quantitative assessment of their contrast. Optiko Electron. Indus. 5, 11–13 (1980)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yelmanov, S., Romanyshyn, Y. (2021). New Technique of Recursive Mean-Separate Contrast Stretching for Image Enhancement. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_73
Download citation
DOI: https://doi.org/10.1007/978-3-030-63270-0_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63269-4
Online ISBN: 978-3-030-63270-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)