Hihaze: One-Step Diffusion for High-Resolution Real-World Image Dehazing
To address these issues, we propose Hihaze, a one-step diffusion framework for high-resolution real-world dehazing.
ProofPaper ↗
Key points
- Diffusion models have recently achieved encouraging results in real-world image dehazing.
- However, restoring high-resolution images remains challenging due to spatially non-uniform haze and inconsistent predictions across local patches.
- Hihaze adopts two haze-aware modulation modules: attentive skip compensation (ASC) and a spatial degradation adapter (SDA).
- ASC selectively transfers reliable encoder information to the variational autoencoder (VAE) decoder, preserving structural details while suppressing haze-degraded features.
Sources (1)
- [1]Hihaze: One-Step Diffusion for High-Resolution Real-World Image DehazingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:46 AM
To address these issues, we propose Hihaze, a one-step diffusion framework for high-resolution real-world dehazing.
Diffusion models have recently achieved encouraging results in real-world image dehazing.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 19, 2026RoAR-VAE: Improving Latent Diffusability through Robust Prediction
- Oct 18, 2026Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
- Oct 18, 2026Test-Time Scaling of Text-to-Image Flow Matching Models with Noise-Rejuvenated Sequential Monte Carlo
- Oct 18, 2026PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
- Oct 16, 2026[Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)
- Oct 16, 2026MOSS-Image (5B, Apache 2.0) on a 12 GB RTX 3060: INT8 quant + lodestones' 8-step DMD turbo in ComfyUI - 35 hard prompts, one seed, no cherry-picking