Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

Saluky Saluky, Yoni Marine, Nurul Bahiyah

Abstract


Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.


Keywords


histogram normalization, contrast enhancement, image, image quality

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References


N. Chumuang, M. Ketcham, and T. Yingthawornsuk, “CCTV based surveillance system for railway station security,” in 2018 International Conference on Digital Arts, Media and Technology (ICDAMT), Phayao, Feb. 2018, pp. 7–12. doi: 10.1109/ICDAMT.2018.8376486.

Saluky, S. H. Supangkat, and F. F. Lubis, “Moving Image Interpretation Models to Support City Analysis,” in 2018 International Conference on ICT for Smart Society (ICISS), Semarang, Oct. 2018, pp. 1–5. doi: 10.1109/ICTSS.2018.8550012.

H. Aditya, T. Gayatri, T. Santosh, S. Ankalaki, and J. Majumdar, “Performance analysis of video segmentation,” in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, Jan. 2017, pp. 1–6. doi: 10.1109/ICACCS.2017.8014567.

S. Saluky, “A Survey on Abandoned Objects Detection from CCTV Surveillance,” ITEJ Inf. Technol. Eng. J., vol. 5, no. 2, pp. 105–118, Dec. 2020, doi: 10.24235/itej.v5i2.53.

E. S. Yelmanova and Y. M. Romanyshyn, “Adaptive enhancement of monochrome images with low-contrast objects,” in 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Sep. 2017, pp. 421–424. doi: 10.1109/STC-CSIT.2017.8098820.

Y. Zhu, Y. Wang, X. Tao, and W. Tang, “Utility of apparent diffusion coefficient histogram analysis in differentiating benign and malignant palate lesions,” Eur. J. Radiol., vol. 157, p. 110566, Dec. 2022, doi: 10.1016/j.ejrad.2022.110566.

T. Njølstad, K. Jensen, A. Dybwad, Ø. Salvesen, H. K. Andersen, and A. Schulz, “Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom,” Eur. J. Radiol. Open, vol. 9, p. 100418, 2022, doi: 10.1016/j.ejro.2022.100418.

L. Zhang, W. Zhou, J. Li, J. Li, and X. Lou, “Histogram of Oriented Gradients Feature Extraction Without Normalization,” in 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Ha Long, Vietnam, Dec. 2020, pp. 252–255. doi: 10.1109/APCCAS50809.2020.9301715.

A. Shifa et al., “Joint Crypto-Stego Scheme for Enhanced Image Protection With Nearest-Centroid Clustering,” IEEE Access, vol. 6, pp. 16189–16206, 2018, doi: 10.1109/ACCESS.2018.2815037.

Aliaa A. A. Youssif, Atef Z. Ghalwash, and Amr S. Ghoneim, “Comparative Study of Contrast Enhancement and Illumination Equalization Methods for Retinal Vasculature Segmentation,” PROC CAIRO Int. Biomed. Eng. Conf. 2006©.

K. Akila, L. S. Jayashree, and A. Vasuki, “Mammographic Image Enhancement Using Indirect Contrast Enhancement Techniques – A Comparative Study,” Procedia Comput. Sci., vol. 47, pp. 255–261, 2015, doi: 10.1016/j.procs.2015.03.205.

C. Liu, X. Sui, X. Kuang, Y. Liu, G. Gu, and Q. Chen, “Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification,” Remote Sens., vol. 11, no. 7, p. 849, Apr. 2019, doi: 10.3390/rs11070849.

L. Maurya, V. Lohchab, P. Kumar Mahapatra, and J. Abonyi, “Contrast and brightness balance in image enhancement using Cuckoo Search-optimized image fusion,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 7247–7258, Oct. 2022, doi: 10.1016/j.jksuci.2021.07.008.

J. Saenpaen, S. Arwatchananukul, and N. Aunsri, “A Comparison of Image Enhancement Methods for Lumbar Spine X-ray Image,” in 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Rai, Thailand, Jul. 2018, pp. 798–801. doi: 10.1109/ECTICon.2018.8620040.

Z. Nabi, R. Chavan, M. Ramchandani, S. Darisetty, and D. N. Reddy, “Endoscopic submucosal dissection and tunneling procedures using novel image-enhanced technique,” VideoGIE, vol. 7, no. 4, pp. 158–163, Apr. 2022, doi: 10.1016/j.vgie.2021.11.005.

T. Kaur and R. K. Sidhu, “Performance Evaluation of Fuzzy and Histogram Based Color Image Enhancement,” Procedia Comput. Sci., vol. 58, pp. 470–477, 2015, doi: 10.1016/j.procs.2015.08.009.

Q. Chen et al., “A Survey on an Emerging Area: Deep Learning for Smart City Data,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 3, no. 5, pp. 392–410, Oct. 2019, doi: 10.1109/TETCI.2019.2907718.

S. Agrawal, R. Panda, P. K. Mishro, and A. Abraham, “A novel joint histogram equalization based image contrast enhancement,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 4, pp. 1172–1182, Apr. 2022, doi: 10.1016/j.jksuci.2019.05.010.

F. Daumas-Ladouce, M. García-Torres, J. L. Vázquez Noguera, D. P. Pinto-Roa, and H. Legal-Ayala, “Multi-Objective Pareto Histogram Equalization,” Electron. Notes Theor. Comput. Sci., vol. 349, pp. 3–23, Jun. 2020, doi: 10.1016/j.entcs.2020.02.010.

K. Lin, S.-C. Chen, C.-S. Chen, D.-T. Lin, and Y.-P. Hung, “Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance,” IEEE Trans. Inf. Forensics Secur., vol. 10, no. 7, pp. 1359–1370, Jul. 2015, doi: 10.1109/TIFS.2015.2408263.




DOI: https://doi.org/10.30591/jpit.v8i3.4929

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