ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing

Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNNs 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. For efficient inference, we design a frequency-spatial decoupled feed-forward backbone. Within this backbone, a Channel-Spatial Laplacian Mask (CSLM) filters haze-amplified noise to extract purified structural details, while Lightweight Global Context Blocks (LGCBs) establish long-range spatial dependencies to capture global haze variations. A Dynamic Feature Arbitration Block (DFAB) then adaptively fuses these semantic and structural features for robust reconstruction. To provide the backbone with degradation-aware priors without increasing inference cost, we introduce a Zero-Inference Prior Propagation Head (ZI-PPH) during training. ZI-PPH leverages a conditional diffusion process to predict residual noise and regularize the shared representation. By discarding the diffusion branch at test time, ZID-Net integrates the representation-shaping benefits of diffusion supervision into a pure feed-forward architecture. Extensive experiments on synthetic, real-world, and remote-sensing benchmarks demonstrate robust restoration across diverse haze conditions, with particularly strong performance on StateHaze1k, where ZID-Net achieves an average PSNR gain of 3.06 dB while maintaining efficient deterministic inference at 19.35 ms.