Image Brightness Improvement Analysis Using HE, AHE, and ESIHE Comparison Methods
DOI:
https://doi.org/10.26623/transformatika.v18i1.2370Keywords:
Image Improvement, Exposure Sub-Image Histogram Equalization, EntropyAbstract
Image improvement is the process to improve visual quality, from the original image to get optimal image results. The first category, this technique operates on the transformation of frequency selection and the second technique operates directly at the pixel level of the image. in this study the Exposure Sub-Image Histogram Equalization (ESIHE) technique will be enhanced with a brightness level to get visual image results. Then ESIHE is compared with other additional techniques, such as Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) from the side of the image. Furthermore, for the even distribution of histograms, we will use ESIHE entropy calculations to show an increase in the image results that are more optimal when compared with HE and AHE. The visual image quality of each technique shows the strength of the method and the superiority of the other methods for various types of images.References
R. C. Gonzales, R. E. Woods, "Digital Image Processing", second ed. Prentice-Hall, 2002.
Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogramequalization", IEEE Trans. Cons. Elec., vol. 43, no. 1, pp. 1 8, 1997.
Y. Wan, Q. Chen, B. M. Zhang, "Image enhancement based on equal areadualistic sub-image histogram equalization method", IEEE Trans. Cons. Elec., vol. 45, no. 1, pp. 68-75, 1999.
S. D. Chen, A. R. Ramli, "Contrast enhancement using recursive mean-separatehistogram equalization for scalable brightness preservation", IEEE Trans. Cons. Elec., vol. 49, no. 4, pp. 1301-1309, 2003a.
S. D. Chen, A. R. Ramli, "Minimum mean brightness error bi-histogramequalization in contrast enhancement", IEEE Trans. Cons. Elec., vol. 49, no. 4, pp. 1310-1319, 2003b.
K. S. Sim, C. P. Tso, Y. Y. Tan, "Recursive sub-image histogram equalizationapplied to gray scale images", Pattern Recogn. Lett., vol. 28, no. 10, pp. 1209-1221, 2007.
S. D. Chen, A New Image Quality Measure for Assessment of Histogram Equalization-Based Contrast Enhancement , Digital Signal Process, vol. 22, pp. 640-647, Apr. 2007.
M. Hanmandlu, N. K. Kumar, M. Kulkarni, "A novel optimal fuzzy system for color image enhancement using bacterial foraging", IEEE Trans. Inst. Meas. vol. 58, no. 8, pp. 2867-2879, 2009.
C. H. Ooi, N. S. P. Kong, H. Ibrahim, "Bi-histogram equalization with a plateaulimit for digital image enhancement", IEEE Trans. Cons. Elec. vol. 55, no. 4, pp. 2072 2080, 2009.
K. Singh, R. Kapoor, "Image enhancement using Exposure based Sub Image HistogramEqualization", Pattern Recognition Letters, vol. 36, pp. 10 14, 2014.
C. Soong-Der, "A new image quality measure for assessment of histogram equalization-based contrast enhancement techniques", Digital Signal Processing, vol.22, pp. 640 647, 2012.
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