Synergistic fusion: An integrated pipeline of CLAHE, YOLO models, and advanced super-resolution for enhanced thermal eye detection

Accurate eye detection in thermal images is essential for diverse applications, including biometrics, healthcare, driver monitoring, and human-computer interaction. However, achieving this accuracy is often hindered by the inherent limitations of thermal data, such as low resolution and poor contrast. This work addresses these challenges by proposing a novel, multifaceted approach that combines both deep learning and image processing techniques. We first introduce a unique dataset of thermal facial images captured with meticulous eye location annotations. To improve image clarity, we employ Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, we explore the effectiveness of advanced YOLO models (YOLOv8 and YOLOv9) for accurate eye detection. Our experiments reveal that YOLOv8 with CLAHE-enhanced images achieved the highest accuracy (precision and recall of 1, mAP50 of 0.995, and mAP50-95 of 0.801), the YOLOv9 model also demonstrated excellent performance with a precision of 0.998, recall of 0.998, mAP-50 of 0.995, and mAP50-95 of 0.753. Furthermore, to enhance the resolution of detected eye regions, we investigate various super-resolution techniques, ranging from traditional methods like Bicubic interpolation to cutting-edge approaches like generative adversarial networks (BSRGAN, ESRGAN) and advanced models like Real-ESRGAN, SwinIR, and SwinIR-Large with ResShift. The performance of these techniques is evaluated using both objective and subjective quality measures. Overall, this work demonstrates the effectiveness of our proposed pipeline, which seamlessly integrates image enhancement, deep learning, and super-resolution techniques. This synergic fusion significantly improves the contrast, accuracy of eye detection, and overall resolution of thermal images, paving the way for potential applications across various fields.