Lightweight Computation Single-Image Fog Removal Based on a New Improved Adaptive Dark Channel Prior

This paper addresses the challenge of image quality degradation in intelligent vehicle systems caused by adverse weather conditions, particularly fog, on embedded platforms. It proposes an optimized dark channel prior (DCP)-based fog removal algorithm, optimized for real-time processing on resource-constrained platforms, such as those equipped with a single neural processing unit (NPU). The algorithm introduces an adaptive mechanism to address overexposure issues commonly found in regions with sky or sun, ensuring robust performance across diverse hazy conditions and exposure levels. Tailored for intelligent vehicle applications, the method supports real-time processing of high-definition video, delivering a speed 151.14 times faster than state-of-The-Art (SoTA) learning-based approaches like FFA-Net and DehazeNet, while achieving a structural similarity index (SSIM) of up to 0.914 compared to reference fog-free images. Moreover, it significantly enhances critical vision-based tasks, such as YOLOv4 object detection, improving object detection accuracy by up to 18%. This lightweight and efficient solution makes it particularly suitable for deployment in advanced driver assistance systems (ADAS) and autonomous navigation platforms, contributing to improved safety and reliability in foggy conditions.