Clarity Amidst Blur: A Deterministic Method for Synthetic Generation of Water Droplets on Camera Lenses

In computer vision, image clarity is crucial, particularly when challenges like water droplets on camera lenses can significantly impair the accurate analysis of visual data. While existing methods mainly focus on small droplets, the impact of larger droplets has been largely overlooked. This paper introduces a novel approach that models water droplets using randomly generated points and Bézier curves to simulate their shape on the lens. Based on this geometric framework, we developed a classifier to distinguish between two visual scenarios within the droplet. For larger droplets, we use a heuristic method to simulate various lens block-ages. We evaluate this simulation framework using a real stereo dataset from [19] with clear and soiled images. Our method, evaluated using Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM), demonstrates superior performance in MSE, while also achieving com-petitive results in SSIM compared to existing techniques for generating realistic water droplets. Additionally, we applied this technique for data augmentation in object detection tasks using the YOLOv7 [25] model. The results show improved robustness, especially in challenging conditions where large droplets obstruct the lens.