PRISM通过物理建模统一恢复真实雾霾图像，提升去雾可解释性。
PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing
- 基于大气散射模型联合重建清晰图像与散射参数，增强过程可解释性。
- 在真实数据集上达到领先性能，有效处理非均匀雾霾和颜色偏移。
- 适用于缺乏成对数据的真实场景去雾，适合实际应用与研究部署。
真实世界图像去雾（RID）旨在去除真实场景中由雾霾引起的退化。由于雾霾分布不均、空间变化的颜色偏移以及成对真实雾霾-清晰图像数据稀缺，该任务仍具挑战性。在PRISM中，我们提出近似散射大气重建（PSAR），一个基于物理结构的框架，可在大气散射模型下联合重建清晰图像与散射变量，使复杂真实条件下复原过程更具可解释性。为弥合合成数据到真实数据的差距，我们设计了在线非均匀雾霾合成管道和选择性自蒸馏适配（SSDA）方案，使模型能从高质量感知目标中选择性学习，并利用其内在散射理解来检测残留雾霾并引导自我优化。在真实世界基准测试上的实验表明，PRISM在去雾任务中取得了具有竞争力的性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。