HiHaze: One-Step Diffusion for High-Resolution Real-World Image Dehazing

Real-world haze is often spatially non-uniform, while the high resolution of modern images imposes substantial computational and memory costs on diffusion-based dehazing. Although one-step latent diffusion provides an efficient alternative to iterative sampling, two challenges remain. First, latent compression and spatially coarse conditioning can weaken local degradation variations, making it difficult to adapt restoration strength to different haze densities. Second, high-resolution inference commonly relies on independently processed patches, which can introduce visible seams and inconsistent color or dehazing strength across the image. To address these issues, we propose **HiHaze**, a haze-aware one-step diffusion framework for high-resolution real-world image dehazing. At the model level, we introduce **Haze-Aware Feature Modulation (HFM)**, consisting of two complementary designs. **Attentive Skip Compensation (ASC)** selectively retrieves structural information from VAE encoder features according to decoder queries, compensating for compression loss while suppressing the direct propagation of haze degradation. **Spatial Degradation Adapter (SDA)** encodes multiple degradation cues into multi-scale spatial features and injects them into the denoising network, enabling region-adaptive restoration for non-uniform haze. For high-resolution inference, we further propose **Global-Guided Patch Fusion (GPF)**, a training-free strategy that combines globally coherent low-frequency information from an additional downsampled full-image prediction with high-frequency details recovered from local patches, while adaptively preserving reliable textures according to local structural consistency. Extensive experiments on multiple real-world dehazing benchmarks demonstrate that HiHaze achieves strong restoration quality and perceptual fidelity while retaining the efficiency of one-step diffusion.