Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions

TL;DR

This paper introduces a unified model and a novel network for image restoration across various adverse weather conditions, leveraging physical weather priors to improve scene recovery.

Contribution

It presents a unified imaging model considering weather-specific physical effects and a weather-prior-based network for enhanced scene restoration.

Findings

Outperforms state-of-the-art methods in multiple adverse weather scenarios.

Effectively models weather-specific visual factors like particles and fog.

Leverages physical priors to improve feature enhancement during restoration.

Abstract

Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather-related artifacts vary with the particle's distance to the camera according to the established scene visibility analysis, where close and faraway regions are more affected by falling drops and fog effects, respectively. Existing methods fail to consider this weather-specific physical visual process; thus, the restoration performance is limited. In this work, we analyze the common visual factors in adverse weather conditions and present a unified imaging model that considers the individually visible particles and fog-like aggregate scattering effects. Further, we design a novel weather-prior-based network, which leverages the weather-related prior information to help recover the scene by enhancing the features using the estimated occlusion and transmission. Experimental results in multiple adverse scenarios show the superiority of our method against state-of-the-art methods.