Image Restoration During Foggy and Rainy Weather : Applications for Driver Assistance Systems
type: Dataset
description: Advanced Driver Assistance Systems (ADAS) are designed to assist the driver and in particular to improve road safety. For this purpose, various sensors are typically embedded in vehicles in order, for example, to alert the driver in case of imminent danger on the road. The use of camera type of sensor is a cost-effective solution and many ADAS based on camera are being created. Unfortunately, the performance of such systems decrease drastically in the presence of adverse weather conditions, especially in the presence of fog or rain, which could oblige to turn off the systems temporarily in order to avoid erroneous results. While, it is precisely in these difficult circumstances that the driver would potentially need the most to be assisted. Once the weather conditions detected and characterized by embedded vision, we propose in this thesis to restore the degraded image to provide a better signal to the ADAS and thus extend the operation range of these systems. In the state of the art, there are several approaches dealing with images restoration, some of which are dedicated to our fog and rain problem and others are more general : denoising, contrast or color enhancement, inpainting... We propose in this work to combine the two families of approaches. In the case of fog our contribution is to take advantage of both approaches (physical and signal) to propose a new automatic method adapted to the case of road images. We evaluated our method using ad hoc criteria (ROC curves, visible contrast to 5 %, assessment on ADAS) applied to databases of synthetic and real images. In case of rain, once the drops present on the windshield are detected, we reconstruct the hidden parts of the image using a method of inpainting based on partial differential equations. The method parameters have been optimized on road images. Finally, we show that it is possible with this approach to build three types of applications: preprocessing, processing and assistance. In every family, we have …
issued: 2026-05-10T20:24:21.674894
language: fr
modified: 2026-05-10T20:24:21.674899
contact point: Organization
  fn: CCSD
keyword: adas
keyword: camera-embarquee
keyword: computer-vision
keyword: detection-de-lespace-navigable
keyword: detection-de-panneaux
keyword: detection-du-brouillard-et-de-la-pluie
keyword: deweathering
keyword: fog-and-rain-detection
keyword: free-space-detection
keyword: image-restoration-and-denoising
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: inpainting
keyword: onboard-camera
keyword: perception-time
keyword: restauration-et-debruitage-dimages
keyword: signal-and-image-processing
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: temps-de-perception
keyword: theses
keyword: traffic-signs-detection
keyword: traitement-du-signal-et-dimages
keyword: vision-par-ordinateur
landing page: https://theses.hal.science/tel-00830869

Image Restoration During Foggy and Rainy Weather : Applications for Driver Assistance Systems
type: Distribution
format: HTML
issued: 2026-05-10T20:24:21.687684
modified: 2026-05-10T20:24:21.653526
access URL: https://theses.hal.science/tel-00830869

test_moissonnage_selune
type: Agent