Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models

TL;DR

Refusion introduces a latent-space diffusion model with enhanced architecture and hyperparameters, enabling high-quality, large-size image restoration across various tasks with improved stability and perceptual performance.

Contribution

The paper proposes a novel U-Net based latent diffusion model that improves stability and accuracy in large-size image restoration without adversarial training.

Findings

Achieves state-of-the-art perceptual performance in shadow removal

Handles large images up to 6000x4000 pixels effectively

Wins 2nd place in NTIRE 2023 Image Shadow Removal Challenge

Abstract

This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network architecture, noise level, denoising steps, training image size, and optimizer/scheduler. We show that tuning these hyperparameters allows us to achieve better performance on both distortion and perceptual scores. We also propose a U-Net based latent diffusion model which performs diffusion in a low-resolution latent space while preserving high-resolution information from the original input for the decoding process. Compared to the previous latent-diffusion model which trains a VAE-GAN to compress the image, our proposed U-Net compression strategy is significantly more stable and can recover highly accurate images without relying on adversarial optimization. Importantly, these modifications allow us to apply diffusion models to various image restoration tasks, including real-world shadow removal, HR non-homogeneous dehazing, stereo super-resolution, and bokeh effect transformation. By simply replacing the datasets and slightly changing the noise network, our model, named Refusion, is able to deal with large-size images (e.g., 6000 x 4000 x 3 in HR dehazing) and produces good results on all the above restoration problems. Our Refusion achieves the best perceptual performance in the NTIRE 2023 Image Shadow Removal Challenge and wins 2nd place overall.