The image enhancement is the most prominent topic among researchers to introduce more amendments in this domain. The image enhancement covers a large number of techniques, mechanisms and ways to enhance the image. The contrast enhancement or to improve the brightness of the image is one of the way to improve the quality of the image. This study develops a novel approach for image contrast enhancement by considering HSI color model to extract the Hue, Saturation and Intensity of the image. Then the fuzzy inference system is applied to improve the intensity of the image pixels. The simulation is done by considering a set of four different images. The performance of the proposed work is evaluated in the terms of Detail Variance and Background Variance. The proposed work is compared with the traditional GLE (Global Local Image Enhancement), Enhanced AHE (Adaptive Histogram Equalization) and Original Image. The simulation results delineates that the proposed work performs outstanding in comparison to the tradition GLE, Enhanced AHE and original image.

Image Enhancement, Contrast Enhancement, Color Model, HIS Model, Fuzzy Inference Model

[1]. Anureet Kaur et al, “Region of Interest based Contrast Enhancement Techniques for CT images”, 2016 Second International Conference on Computational Intelligence & Communication Technology, Pp. 60-63, 2016.
[2]. Akshay Girdhar, Savita Gupta and Jaskaran Bhullar, “Region Based Adaptive Contrast Enhancement of Medical Ultrasound Images”, Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on, February 2015.
[3]. A. Djerouni, H. Hamada, and N. Berrached, “MR imaging contrast enhancement and segmentation using fuzzy clustering”, International Journal of Computer Science Issues, Vol. 8, No 2, Pp. 392-401, July 2011.
[4]. Makandar, A., & Halalli, B, “Image enhancement techniques using highpass and lowpass filters”, International Journal of Computer Applications, Vol 109, Issue 14, 2015.
[5]. Chin Yeow Wong, Shilong Liu, San Chi Liu, Md Arifur Rahman, Stephen Ching-Feng Lin, Guannan Jiang, Ngaiming Kwok and Haiyan Shi, “Image contrast enhancement using histogram equalization with maximum intensity coverage”, Journal of Modern Optics, Vol. 63, No. 16, Pp. 1618-1629, March 2016.
[6]. Cao, G., Huang, L., Tian, H., Huang, X., Wang, Y., & Zhi, R., “Contrast enhancement of brightness-distorted images by improved adaptive gamma correction”. Computers & Electrical Engineering, 2017.
[7]. Navdeep Kanwal, Akshay Girdhar and Savita Gupta, “Region Based Adaptive Contrast Enhancement of Medical X-Ray Images”, Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on, May 2011.
[8]. Namita Naik,, “Low Contrast Image Enhancement using Wavelet Transform based Algorithms: A Literature Review” International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Vol.-3, No.6, Pp 123-128, June 2015.
[9]. Ritika and Sandeep Kaur, “Contrast Enhancement Techniques for Images– A Visual Analysis”, International Journal of Computer Applications, Vol. 64, No. 1, Pp. 20-25, February 2013.
[10]. Kaur, R., & Kaur, S. “Comparison of contrast enhancement techniques for a medical image”. In Emerging Devices and Smart Systems (ICEDSS), IEEE, Pp 155-159, 2016.
[11]. Vijay A. Kotkar and Sanjay S. Gharde, “Review Of Various Image Contrast Enhancement Techniques”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 7, Pp. 2786-2793, July 2013 .
[12]. Gopi.P.C, Sharmila.R, Indhumathi.T and Savitha.S, “An Intelligent New Age Method of Image Compression and Enhancement with Denoising for Bio-Medical Application”, IJSRCSE, Vol 1, Issue 4, Pp 12-16, 2013.