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PRISM framework enhances image dehazing with unified reconstruction

Researchers have introduced PRISM, a new framework for reconstructing clear images from hazy real-world scenes. PRISM utilizes a physically structured approach called Proximal Scattering Atmosphere Reconstruction (PSAR) to jointly restore the clear scene and atmospheric scattering variables, enhancing interpretability. To address the lack of paired data, the system includes an online haze synthesis pipeline and a Selective Self-Distillation Adaptation (SSDA) scheme for unpaired learning, enabling self-refinement based on perceptual targets and scattering understanding. Experiments show PRISM achieves competitive results on real-world dehazing benchmarks. AI

RANK_REASON The cluster contains a research paper detailing a new method for image processing. [lever_c_demoted from research: ic=1 ai=1.0]

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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Chengyu Fang, Chunming He, Yuelin Zhang, Chubin Chen, Chenyang Zhu, Hongqiu Wang, Longxiang Tang, Xiu Li, Sina Farsiu ·

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

    arXiv:2604.07048v2 Announce Type: replace 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-c…