GICE: Generalized Image Contrast Enhancement with Diffusion Priors

Real-world photographs often exhibit mixed contrast degradations induced by challenging illumination conditions (e.g., low-light, backlit, and over-exposure) together with local corruptions such as noise and compression, which reduce visibility and obscure fine details. Most existing methods target a single specific enhancement setting, often leading to brittle behavior when multiple degradations co-occur within the image. In this work, we present Generalized Image Contrast Enhancement (GICE), a unified framework that addresses diverse contrast degradations while recovering realistic textures and details. Specifically, we cast generalized contrast enhancement as a conditional image generation problem: we condition a pre-trained diffusion model on a degradation-robust representation to enable stable enhancement under heterogeneous contrast degradations. This representation is learned through multi-exposure supervision and structure-related corruption augmentation, which disentangles scene content from illumination variations and local distortions, thereby providing a stable, task-agnostic condition. Given this condition, we train the GICE model by finetuning on the pre-trained Stable Diffusion model in an end-to-end manner for efficient one-step refinement, improving image local contrast as well as fine details. Extensive experiments demonstrate that GICE generalizes well across diverse lighting conditions and consistently outperforms previous task-specific models. The code and models of GICE will be released.