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arXiv · 2604.07048

PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing

Chengyu Fang·Chunming He·Yuelin Zhang·Chubin Chen·Chenyang Zhu·Hongqiu Wang·Longxiang Tang·Xiu Li·Sina Farsiu
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Abstract

Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying color shifts, and the scarcity of paired real hazy-clean data. In PRISM, we propose Proximal Scattering Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, making the restoration process more interpretable in complex real-world conditions. To bridge the synthetic-to-real gap, we design an online non-uniform haze synthesis pipeline and a Selective Self-Distillation Adaptation (SSDA) scheme for unpaired real-world scenarios, which enables the model to selectively learn from high-quality perceptual targets while leveraging its intrinsic scattering understanding to audit residual haze and guide self-refinement. Experiments on real-world benchmarks demonstrate that PRISM achieves competitive performance on RID tasks.

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Chengyu Fang, Chunming He, Yuelin Zhang, Chubin Chen, Chenyang Zhu, Hongqiu Wang, Longxiang Tang, Xiu Li, Sina Farsiu. 2026-04-08. PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing. https://arxiv.org/abs/2604.07048

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