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Related Experiment Video

Updated: Jul 16, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Enhancing Perception Through Context-Adaptive Visible and SWIR Image Fusion in Harsh Environments.

Alexandre Riffard¹, Mathieu Labussière¹, Pierre Duthon²

¹Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

Autonomous vehicles struggle in bad weather. This study introduces VISWIR, a new fusion method using visible and short-wave infrared sensors to improve perception in fog, rain, and snow.

Keywords:

adverse weather autonomousvehicles image fusion no-reference image quality assessment (NR-IQA)short-wave infrared (SWIR)

Related Experiment Videos

Last Updated: Jul 16, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

Computer Vision
Sensor Fusion
Autonomous Systems

Background:

Autonomous vehicle perception is challenged by adverse weather conditions like fog, rain, and snow.
Short-wave infrared (SWIR) sensors can penetrate atmospheric disturbances, but fusing their data with visible (VIS) cameras is complex due to signal decorrelation and static fusion limitations.

Purpose of the Study:

To develop a robust and lightweight pixel-level image fusion method for enhancing autonomous vehicle perception in adverse weather.
To address the limitations of static fusion schemes by introducing an adaptive parameter scheduling strategy.

Main Methods:

Proposed VISWIR (Visible and SWIR Weighted Image Reconstruction), a pixel-level fusion method utilizing a multi-scale pyramid architecture.
Implemented an automated parameter scheduling strategy based on weather conditions within an optimization framework.
Employed a multi-objective optimization approach maximizing perceptual image quality using No-Reference Image Quality Assessment (NR-IQA) metrics.

Main Results:

VISWIR demonstrated effective fusion of VIS and SWIR data, enhancing image quality in simulated adverse weather.
The automated parameter scheduling adapted fusion hyperparameters based on meteorological context, outperforming static fusion.
Validated in controlled scenarios with varying weather severities, confirming the method's robustness.

Conclusions:

VISWIR offers a promising algorithmic baseline for improving autonomous vehicle perception in challenging weather conditions.
The adaptive fusion approach enhances robustness and perceptual image quality, crucial for safe autonomous driving.
This lightweight method has the potential to significantly advance the reliability of autonomous systems in real-world environments.