FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process
This addresses the need for high-quality post-processing of AI-generated images, offering a practical solution for users of text-to-image models, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of enhancing images generated by text-to-image models by proposing FreeEnhance, a tuning-free method that uses a noising-and-denoising process with pre-trained diffusion models to enrich details while preserving content, achieving state-of-the-art performance on the HPDv2 dataset and higher human preference than Magnific AI.
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.