Estimating Fog Parameters from an Image Sequence using Non-linear Optimisation

Yining Ding, Andrew M. Wallace, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given a sequence of images taken in foggy weather, we seek to estimate the atmospheric light and the scattering coefficient. These are key parameters to characterise the nature of the fog, to reconstruct a clear image (defogging), and to infer scene depth. Existing methods adopt a sequential estimation strategy which is prone to error propagation. In sharp contrast, we take a more systematic approach and jointly estimate these parameters by solving a unified non-linear optimisation problem. Experimental results show that the proposed method is superior to existing ones in terms of both estimation accuracy and precision. Our method further demonstrates how image defogging and depth estimation can be linked to a visual localisation system, contributing to more comprehensive and robust perception in fog.
Original languageEnglish
Title of host publication2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE
Pages1567-1575
Number of pages9
ISBN (Electronic)9798350318920
DOIs
Publication statusPublished - 9 Apr 2024
Event2024 IEEE/CVF Winter Conference on Applications of Computer Vision - Waikoloa, United States
Duration: 3 Jan 20248 Jan 2024

Conference

Conference2024 IEEE/CVF Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period3/01/248/01/24

Keywords

  • 3D computer vision
  • Algorithms
  • Applications
  • Autonomous Driving
  • Low-level and physics-based vision

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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