Comparison of Histogram Equalization and Multi-Scale Retinex Methods for Near-Infrared Image Enhancement in Drowsiness Detection

Computer vision-based drowsiness detection faces significant challenges in low-light conditions, particularly when using near-infrared (NIR) sensors for driver monitoring systems. Appropriate image enhancement methods are crucial to improve detection accuracy. This study systematically evaluates five enhancement methods: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), and Multi-Scale Retinex with Color Restoration (MSRCR). The evaluation was conducted on 4,272 frames from the University of Liège (ULg) Multimodality Drowsiness Database (DROZY) using four no-reference metrics: Natural Image Quality Evaluator (NIQE), Perception-based Image Quality Evaluator (PIQE), Shannon Entropy, and Lightness Order Error (LOE). Additional validation was performed by measuring the face detection rate using MediaPipe. The results show that CLAHE achieves an optimal balance with an NIQE of 4.61 (best natural quality), a detection rate of 97.9%, and an LOE of 0.058 (superior structural preservation). MSRCR produces the highest entropy (6.58) but the lowest detection rate (75.6%), indicating structural distortion in the NIR context. Statistical validation using the Wilcoxon signed-rank test and the Friedman test confirmed the significance of the findings (p < 0.05). CLAHE is recommended for NIR surveillance-based drowsiness detection systems.