John and Marcia Price College of Engineering
21 ClaraVisio: Computational DeFogging via Image-to-Image Translation on a Novel Free-Floating Fog Dataset
Amir Zarandi and Rajesh Menon
Faculty Mentor: Rajesh Menon (Electrical and Computer Engineering, University of Utah)
ABSTRACT
ClaraVisio presents an innovative Image-to-Image (I2I) translation framework designed to transform foggy images into clear ones, addressing a critical challenge in autonomous vehicle technology. Building upon existing models like StereoFog and FogEye, ClaraVisio introduces a unique dataset of free-floating fog, collected using a custom-built setup. This setup, employing a Raspberry Pi and a 3D-printed cone, enhances free-floating fog conditions during image capture. The Pix2Pix model, trained on over a thousand pairs of foggy and clear images, achieved an average multi-scale structural similarity index measure (MS-SSIM) exceeding 90%. This approach, incorporating natural fog variations, contrasts with previous research using controlled fog environments. Despite the relatively small dataset, ClaraVisio demonstrates the effectiveness of this method for enhancing visibility in foggy images and lays the groundwork for further data collection and model refinement.
Introduction
Fog-induced low visibility poses a significant challenge to road safety and the advancement of autonomous driving technology. The Federal Highway Administration reports that fog accounts for over 38,700 vehicle crashes annually on US roads, resulting in approximately 600 fatalities [1]. As the automotive industry progresses towards higher levels of automation, the ability to navigate safely in adverse weather conditions becomes increasingly critical.
The Society of Automotive Engineers (SAE) defines Level 4 autonomy as the first stage where driver engagement is not required during system operation [2]. Conservative estimates project that Level 4 and 5 autonomous vehicles will constitute 8% of US vehicle sales by 2035. However, the struggle of both human drivers and current autonomous systems to operate effectively in fog and other adverse weather conditions remains a major barrier to achieving widespread Level 4 autonomy [3][4].
Computational defogging through Machine Learning (ML) emerges as a promising solution to this challenge. Image-to-Image (I2I) translation offers a powerful approach for converting foggy images to clear ones. Optimal results in I2I translation require paired-image datasets, where each clear image corresponds precisely to a foggy counterpart. However, acquiring such datasets poses significant difficulties, especially when dealing with naturally occurring fog.
Previous work in this field, such as StereoFog by Anton Pollock [5], utilized datasets of paired fogged and clear images and employed the pix2pix I2I ML framework. Similarly, FogEye by Moody, Parke, and Welch [6] implemented High Dynamic Range (HDR) imaging techniques. However, both these studies relied on entrapped fog, which f