PRISM: Rethinking Scattered Atmosphere Reconstruction as a Unified Understanding and Generation Model for Real-world Dehazing

• URL: http://arxiv.org/abs/2604.07048v1
• Date: Wed, 08 Apr 2026 13:01:30 GMT
• Title: PRISM: Rethinking Scattered Atmosphere Reconstruction as a Unified Understanding and Generation Model for Real-world Dehazing
• Abstract summary: PRISM is a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model.<n>We design an online non-uniform haze synthesis pipeline and a Selective Self-distillation Adaptation scheme for unpaired real-world scenarios.<n>Experiments on real-world benchmarks demonstrate that PRISM achieves state-of-the-art performance on RID tasks.
• Score: 22.670768613999787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 illumination from multiple light sources, and the scarcity of paired real hazy-clean data. In PRISM, we propose Proximal Scattered Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, thereby improving reliability in complex regions and mixed-light conditions. To bridge the synthetic-to-real gap, we design an online non-uniform haze synthesis pipeline and a Selective Self-distillation Adaptation 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. Extensive experiments on real-world benchmarks demonstrate that PRISM achieves state-of-the-art performance on RID tasks.

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