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

TL;DR

ZID-Net introduces a decoupled diffusion prior framework for single image dehazing, combining efficiency and robustness by integrating diffusion priors into a feed-forward network without inference latency.

Contribution

The paper proposes a novel decoupled diffusion prior approach that enhances dehazing performance while maintaining real-time efficiency, using a physical prior propagation during training.

Findings

Achieves 40.75 dB PSNR on synthetic datasets.

Outperforms existing methods with a 1.13 dB PSNR gain on real-world datasets.

Runs at 19.35 ms inference time, enabling real-time applications.

Abstract

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. 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 the global variations of haze. A Dynamic Feature Arbitration Block (DFAB) then adaptively fuses these semantic and structural features for robust reconstruction. To provide this backbone with physical priors without the 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, providing degradation-aware structural supervision to the backbone. By discarding the diffusion branch at test time, ZID-Net integrates diffusion priors into a pure feed-forward architecture for accurate and efficient restoration. ZID-Net achieves 40.75 dB PSNR on the synthetic RESIDE dataset and outperforms existing methods with a 1.13 dB gain on real-world datasets. Additionally, it yields a 3.06 dB PSNR gain on the StateHaze1k remote sensing dataset with an inference time of just 19.35 ms. The project code is available at: https://github.com/XoomitLXH/ZID-Net.