GenColor: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation

Color enhancement is a crucial yet challenging task in digital photography. It demands methods that are:

Existing approaches typically fall short in at least one of these aspects, yielding unsatisfactory results.

We propose GenColor, a novel diffusion-based framework for sophisticated, texture-preserving color enhancement. GenColor reframes the task as conditional image generation. Leveraging ControlNet and a tailored training scheme, it learns advanced color transformations that adapt to diverse lighting and content.

We train GenColor on ARTISAN, our newly collected large-scale dataset of 1.2M high-quality photographs specifically curated for enhancement tasks. To overcome texture preservation limitations inherent in diffusion models, we introduce a color-transfer network with a novel degradation scheme that simulates texture–color relationships. This network achieves pixel-perfect texture preservation while enabling fine-grained color matching with the diffusion-generated reference images.

Extensive experiments show that GenColor produces visually compelling results comparable to those of expert colorists and surpasses state-of-the-art methods in both subjective and objective evaluations. We have released the code and dataset.