An image enhancement based method for improving rPPG extraction under low-light illumination

Chen，Shutao1; Wong，Kwan Long1; Chan，Tze Tai1; Wang，Ya2; So，Richard Hau Yue3; Chin，Jing Wei1

2025-02-01

10.1016/j.bspc.2024.106963

Biomedical Signal Processing and Control   影响因子和分区

1746-8094

1746-8108

Remote photoplethysmography (rPPG) has gained significant attention as a non-invasive approach for measuring human vital signs from videos. However, the reliance on capturing the reflection of ambient lighting causes it to be susceptible to the adverse effects of low illumination levels, leading to deterioration in the signal quality. The preliminary study introduces a novel methodology for enhancing rPPG signal extraction in low-light conditions through the integration of an Image Enhancement Model (IEM) inspired by Retinex theory, which significantly improved signal quality by preprocessing video frames to better capture the subtle changes in facial blood flow. Recognizing the challenge posed by the generalization capacity over different deep-learning models and unseen examples from various datasets, this study further evaluated the efficacy of the IEM + rPPG extraction model across multiple datasets (UBFC-rPPG, BH-rPPG, MMPD-rPPG, and VIPL-HR) by integrating IEM with existing deep-learning based rPPG extraction models including DeepPhys, PhysNet, and PhysFormer and traditional POS rPPG extraction method. Our experiments demonstrate a consistent improvement for all the methods in heart rate estimation accuracy across all datasets, underscoring the IEM's adaptability and effectiveness. The paper also explores the application of our method to traditional rPPG extraction techniques, further validating its potential for broader usage. Through comprehensive analysis, this research not only confirms the impact of lighting conditions on rPPG signal quality but also provides a robust solution for more reliable non-invasive vital sign monitoring in diverse environments.

And image enhancement Deep-learning Heart rate estimation Remote photoplethysmography Transfer learning

英语

其他

2-s2.0-85205141144

Scopus

1.Department of Clinical AI,PanopticAI,Hong Kong
2.Department of Statistics and Data Science,Southern University of Science and Technology,China
3.Department of Industrial Engineering and Decision Analytics,The Hong Kong University of Science and Technology,China

Chen，Shutao,Wong，Kwan Long,Chan，Tze Tai,et al. An image enhancement based method for improving rPPG extraction under low-light illumination[J]. Biomedical Signal Processing and Control,2025,100.

Chen，Shutao,Wong，Kwan Long,Chan，Tze Tai,Wang，Ya,So，Richard Hau Yue,&Chin，Jing Wei.(2025).An image enhancement based method for improving rPPG extraction under low-light illumination.Biomedical Signal Processing and Control,100.

Chen，Shutao,et al."An image enhancement based method for improving rPPG extraction under low-light illumination".Biomedical Signal Processing and Control 100(2025).