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

Tim Dieter Eberhardt, Tim Brühl, Robin Schwager, Tin Stribor Sohn, Wilhelm Stork; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5187-5196

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 Bezier 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 blockages. 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 competitive 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.