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

• URL: http://arxiv.org/abs/2510.20266v1
• Date: Thu, 23 Oct 2025 06:46:22 GMT
• Title: GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing
• Authors: Mahtab Movaheddrad, Laurence Palmer, C. -C. Jay Kuo,
• Abstract summary: Image dehazing is a restoration task that aims to recover a clear image from a single hazy input.<n>We propose GUSL-Dehaze, a Green U-Shaped Learning approach to image dehazing.
• Score: 21.49782595218257
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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