Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics
DOI:
https://doi.org/10.30871/jaic.v10i1.12055Keywords:
CLAHE, Histogram Equalization, Image Enhancement, No-ReferenceAbstract
Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
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Copyright (c) 2026 Muhammad Naufal, Harun Al Azies, Farrikh Alzami, Rivaldo Mersis Brilianto
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