AI RESEARCH
ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing
arXiv CS.CV
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ArXi:2604.23709v1 Announce Type: new Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNN networks struggle to learn robust priors for dense and non-homogeneous haze. Conversely, diffusion models provide strong generative priors but suffer from severe inference latency and sampling instability. To address these limitations, we propose ZID-Net, a novel framework that explicitly decouples diffusion supervision from feed-forward inference.