Can Large Pretrained Depth Estimation Models Help With Image Dehazing?

• URL: http://arxiv.org/abs/2508.00698v1
• Date: Fri, 01 Aug 2025 15:14:45 GMT
• Title: Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
• Authors: Hongfei Zhang, Kun Zhou, Ruizheng Wu, Jiangbo Lu,
• Abstract summary: Image dehazing remains a challenging problem due to the spatially varying nature of haze in real-world scenes.<n>We propose a plug-and-play RGB-D fusion module that seamlessly integrates with diverse dehazing architectures.
• Score: 35.4696172315888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image dehazing remains a challenging problem due to the spatially varying nature of haze in real-world scenes. While existing methods have demonstrated the promise of large-scale pretrained models for image dehazing, their architecture-specific designs hinder adaptability across diverse scenarios with different accuracy and efficiency requirements. In this work, we systematically investigate the generalization capability of pretrained depth representations-learned from millions of diverse images-for image dehazing. Our empirical analysis reveals that the learned deep depth features maintain remarkable consistency across varying haze levels. Building on this insight, we propose a plug-and-play RGB-D fusion module that seamlessly integrates with diverse dehazing architectures. Extensive experiments across multiple benchmarks validate both the effectiveness and broad applicability of our approach.

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