Fusion CLAHE-Based Image Enhancement with fuzzy Set Theory on Field Images
-
2018-12-09 https://doi.org/10.14419/ijet.v7i4.31.23730 -
Image Enhancement, CLAHE, Fuzzy, USM. -
Abstract
In this paper, a new fusion of Contrast-Limited Adaptive Histogram Equalisation or CLAHE-based method is proposed to enhance field images. The field images, which are low resolution images, were taken using a camera or other devices such as smartphones with lower quality as compared to the lab images with proper setup. The field images had low contrast and were blurred and unsharp due to inconsistent setting or environment exposures. Image enhancement helps to enrich the perception of images for better quality, reduce impulsive noise, and sharpen the edges with the help of different image enhancement techniques. The main attraction towards the enhancement of this research area is due to the additional knowledge and hidden information provided by the results of this procedure, which will further be used for many different useful purposes. This research proposes a fusion of CLAHE-based with Fuzzy set theory. An optimisation technique was applied to increase the enhancement ratio. The result of the proposed fusion method was compared with the standard method as a benchmark. The obtained value is compared by using image quality measurement techniques. The proposed fusion method produces better quality and enhanced images and required minimum processing time than the other methods.
Â
-
References
[1] Arunpriya, C., & Thanamani, A. S. (2014). A novel leaf recognition technique for plant classification. Int J Comput Eng Appl, 4, 42-55.
[2] Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Computer vision–ECCV 2012 (pp. 502-516). Springer, Berlin, Heidelberg.
[3] Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., & Upparamani, P. S. (2013). A leaf recognition technique for plant classification using RBPNN and Zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 984-988.
[4] Chaki, J., Parekh, R., & Bhattacharya, S. (2015). Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognition Letters, 58, 61-68.
[5] Valliammal, N., & Geethalakshmi, S. N. (2011). A hybrid method for enhancement of plant leaf recognition. World of Computer Science and Information Technology Journal, 1(9), 370-375.
[6] Khirade, S. D., & Patil, A. B. (2015, February). Plant disease detection using image processing. In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on (pp. 768-771). IEEE.
[7] Sharmila, R., & Uma, R. (2011). A new approach to image contrast enhancement using weighted threshold histogram equalization with improved switching median filter. International Journal of Advanced Engineering Sciences and Technologies, 7(2), 208-211.
[8] Namdeo, A., & Bhadoriya, S. S. (2016) A Review on Image Enhancement Techniques with its Advantages and Disadvantages. IJSART 2395-1052.
[9] Singh, B. B., & Patel, S. (2017). Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion. International Journal of Computer Applications, 167(5).
Hasikin, K., & Isa, N. A. M. (2014). Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. Signal, Image and Video Processing, 8(8), 1591-1603.
-
Downloads
-
How to Cite
Albahari, E., Madzin, H., & Roff Mohd Noor, M. (2018). Fusion CLAHE-Based Image Enhancement with fuzzy Set Theory on Field Images. International Journal of Engineering & Technology, 7(4.31), 465-468. https://doi.org/10.14419/ijet.v7i4.31.23730Received date: 2018-12-12
Accepted date: 2018-12-12
Published date: 2018-12-09