Keywords
HE
Kontras Citra
RMSE
PSNR
How to Cite
Abstract
Penelitian ini berfokus pada implementasi Histogram Equalization (HE) dalam pengenalan wajah, yang bertujuan untuk meningkatkan kontras citra wajah guna memperbaiki akurasi identifikasi. Tantangan yang dihadapi adalah pencahayaan tidak merata dan noise akibat overenchancement. Solusi yang diusulkan adalah penerapan Histogram Equalization, yang meliputi perhitungan histogram dan pemetaan nilai intensitas untuk menghasilkan citra yang lebih jelas. Hasil penelitian ini menunjukkan bahwa Histogram Equalization berhasil meningkatkan kualitas citra, dengan nilai RMSE (Root Mean Square Error) berkisar antara 9.40 hingga 10.48 dan PSNR (Peak Signal-to-Noise Ratio) berkisar antara 27.27 hingga 28.67. hubungan anatar RMSE dan PSNR menunjukkan bahwa semakin rendah nilai RMSE, semakin tinggi nilai RMSE, semakin tinggi nilai PSNR, yang menandakan bahwa citra wajah hasil olahan lebih mendekati citra asli dan kualitasnya tetap baik.
References
[1] S. M. Pizer et al., “ADAPTIVE HISTOGRAM EQUALIZATION AND ITS VARIATIONS.,” Comput Vis Graph Image Process, vol. 39, no. 3, 1987, doi: 10.1016/S0734-189X(87)80186-X.
[2] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Histogram Equalization Variants as Optimization Problems: A Review,” Archives of Computational Methods in Engineering, vol. 28, no. 3, 2021, doi: 10.1007/s11831-020-09425-1.
[3] N. Ahmad and A. Hadinegoro, “Metode histogram equalization untuk perbaikan citra digital,” Semantik 2012, pp. 437–443, 2012.
[4] H. Yeganeh, A. Ziaei, and A. Rezaie, “A novel approach for contrast enhancement based on histogram equalization,” in Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development, 2008. doi: 10.1109/ICCCE.2008.4580607.
[5] W. A. Mustafa and M. M. M. Abdul Kader, “A Review of Histogram Equalization Techniques in Image Enhancement Application,” in Journal of Physics: Conference Series, 2018. doi: 10.1088/1742-6596/1019/1/012026.
[6] J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Transactions on Image Processing, vol. 9, no. 5, 2000, doi: 10.1109/83.841534.
[7] M. Hassan, M. Suhail Shaikh, and M. A. Jatoi, “Image quality measurement-based comparative analysis of illumination compensation methods for face image normalization,” Multimed Syst, vol. 28, no. 2, pp. 511–520, 2022, doi: 10.1007/s00530-021-00853-y.
[8] P. Musa, F. Al Rafi, and M. Lamsani, “A review: Contrast-limited adaptive histogram equalization (CLAHE) methods to help the application of face recognition,” in Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 2018. doi: 10.1109/IAC.2018.8780492.
[9] R. M. Dyke and K. Hormann, “Histogram equalization using a selective filter,” Visual Computer, vol. 39, no. 12, 2023, doi: 10.1007/s00371-022-02723-8.
[10] R. Dorothy, R. M. Joany, R. J. Rathish, S. Santhana Prabha, and S. Rajendran, “Image enhancement by Histogram equalization,” Int. J. Nano. Corr. Sci. Engg, vol. 2, no. 4, 2015.
[11] T. Kristantio, D. P. Pamungkas, and R. Wulanningrum, “Analisa Hasil Perbaikan Citra Menggunakan Histogram Equalization,” in Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 2023, pp. 505–511.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2025 Shalaisha Amelia Putri Gemini, Resty Wulanningrum, Siti Rochana