Abstract
Image enhancement is the process of modifying digital images so that results are suitable for human perception. An upcoming need for image visualization during all lighting conditions by the use of infrared (IR) imagery has gained momentum. It is deemed fit for efficient target acquisition and object deduction. However, due to low image resolution and difficulty in spotting certain objects whose temperature is similar to that of the ground, infrared images must be subjected to further enhancement. Our given proposal aims to enhance infrared images, making use of the fuzzy-based enhancement technique (FBE), and to compare its efficacy with other techniques such as histogram equalization (HE), adaptive histogram equalization (AHE), max–median filter, and multi-scale top-hat transform. The enhanced image is then analyzed using different quantitative metrics such as peak signal-to-noise ratio (PSNR), image quality index (IQI), and structural similarity (SSIM) for performance evaluation. From experimental results, it is concluded that FBE results in the best quality image.
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References
Rajkumar, S., Chandra Mouli, P.V.S.S.R.: Target detection in infrared images using block-based approach. In: Informatics and Communication Technologies for Societal Development, pp. 9–16. Springer India (2015)
Gonzalez, R.C.: Digital Image Processing. Pearson Education India (2009)
Kim, Y.-T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)
Chen, S.-D., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49(4), 1310–1319 (2003)
Zuo, C., Chen, Q., Sui, X.: Range limited bi-histogram equalization for image contrast enhancement. Opt. Int. J. Light Electron Opt. 124(5), 425–431 (2013)
Wang, B., et al.: A real-time contrast enhancement algorithm for infrared images based on plateau histogram. Infrared Phys. Technol. 48(1), 77–82 (2006)
Lin, C.-L.: An approach to adaptive infrared image enhancement for long-range surveillance. Infrared Phys. Technol. 54(2), 84–91 (2011)
Liang, K., et al.: A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Phys. Technol. 55(4), 309–315 (2012)
Deshpande, S.D., et al.: Max-mean and max-median filters for detection of small targets. In: SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics (1999)
Zhao, J., Qu, S.: The fuzzy nonlinear enhancement algorithm of infrared image based on curvelet transform. Proc. Eng. 15, 3754–3758 (2011)
Bai, X., Zhou, F., Xue, B.: Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform. Infrared Phys. Technol. 54(2), 61–69 (2011)
Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
Serra, J. Image Analysis and Mathematical Morphology. Academic Press, Inc. (1983)
Soundrapandiyan, R., Chandra Mouli, P.V.S.S.R.: Perceptual Visualization Enhancement of Infrared Images Using Fuzzy Sets. Transactions on Computational Science XXV, pp. 3–19. Springer, Berlin (2015)
Sayood, K.: Introduction to data compression. Newnes (2012)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)
Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Lewis, J.P.: Fast normalized cross-correlation. In: Vision Interface, vol. 10, no. 1 (1995)
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Rajkumar, S., Dutta, P., Trivedi, A. (2018). Adaptive Infrared Images Enhancement Using Fuzzy-Based Concepts. In: Agrawal, S., Devi, A., Wason, R., Bansal, P. (eds) Speech and Language Processing for Human-Machine Communications. Advances in Intelligent Systems and Computing, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-6626-9_13
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DOI: https://doi.org/10.1007/978-981-10-6626-9_13
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