FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process

TL;DR

FreeEnhance is a tuning-free image enhancement framework that leverages diffusion models with a content-consistent noising-and-denoising process, significantly improving visual quality and detail preservation without additional training.

Contribution

It introduces a novel two-stage, tuning-free enhancement method using off-the-shelf diffusion models with region-aware noising and property-based regularization for content preservation.

Findings

Outperforms state-of-the-art models on HPDv2 dataset

Achieves higher human preference over commercial solutions

Enhances images with high acutance and visual quality

Abstract

The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of the generated images. Exploring diffusion models to enhance the generated images nevertheless is not trivial and necessitates to delicately enrich plentiful details while preserving the visual appearance of key content in the original image. In this paper, we propose a novel framework, namely FreeEnhance, for content-consistent image enhancement using the off-the-shelf image diffusion models. Technically, FreeEnhance is a two-stage process that firstly adds random noise to the input image and then capitalizes on a pre-trained image diffusion model (i.e., Latent Diffusion Models) to denoise and enhance the image details. In the noising stage, FreeEnhance is devised to add lighter noise to the region with higher frequency to preserve the high-frequent patterns (e.g., edge, corner) in the original image. In the denoising stage, we present three target properties as constraints to regularize the predicted noise, enhancing images with high acutance and high visual quality. Extensive experiments conducted on the HPDv2 dataset demonstrate that our FreeEnhance outperforms the state-of-the-art image enhancement models in terms of quantitative metrics and human preference. More remarkably, FreeEnhance also shows higher human preference compared to the commercial image enhancement solution of Magnific AI.