GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing

TL;DR

GUSL-Dehaze introduces a lightweight, physics-based, U-shaped learning framework for image dehazing that avoids deep neural networks, reducing complexity while maintaining competitive performance.

Contribution

It presents a novel green learning approach combining physics-based modeling with a U-shaped architecture for efficient, interpretable image dehazing without deep learning.

Findings

Achieves state-of-the-art dehazing performance with fewer parameters.

Reduces computational costs compared to deep learning models.

Maintains high interpretability and effectiveness in resource-constrained environments.

Abstract

Image dehazing is a restoration task that aims to recover a clear image from a single hazy input. Traditional approaches rely on statistical priors and the physics-based atmospheric scattering model to reconstruct the haze-free image. While recent state-of-the-art methods are predominantly based on deep learning architectures, these models often involve high computational costs and large parameter sizes, making them unsuitable for resource-constrained devices. In this work, we propose GUSL-Dehaze, a Green U-Shaped Learning approach to image dehazing. Our method integrates a physics-based model with a green learning (GL) framework, offering a lightweight, transparent alternative to conventional deep learning techniques. Unlike neural network-based solutions, GUSL-Dehaze completely avoids deep learning. Instead, we begin with an initial dehazing step using a modified Dark Channel Prior (DCP), which is followed by a green learning pipeline implemented through a U-shaped architecture. This architecture employs unsupervised representation learning for effective feature extraction, together with feature-engineering techniques such as the Relevant Feature Test (RFT) and the Least-Squares Normal Transform (LNT) to maintain a compact model size. Finally, the dehazed image is obtained via a transparent supervised learning strategy. GUSL-Dehaze significantly reduces parameter count while ensuring mathematical interpretability and achieving performance on par with state-of-the-art deep learning models.