LEDiff: Latent Exposure Diffusion for HDR Generation
This addresses the need for HDR content in applications like image generation and lighting for users of consumer displays and generative AI, though it appears incremental as it builds on existing diffusion models and exposure fusion techniques.
The paper tackles the problem of generating high dynamic range (HDR) content from low dynamic range (LDR) images, which is limited in existing methods, by introducing LEDiff, a method that enables HDR generation and conversion with photorealistic details and physically-plausible dynamic range.
While consumer displays increasingly support more than 10 stops of dynamic range, most image assets such as internet photographs and generative AI content remain limited to 8-bit low dynamic range (LDR), constraining their utility across high dynamic range (HDR) applications. Currently, no generative model can produce high-bit, high-dynamic range content in a generalizable way. Existing LDR-to-HDR conversion methods often struggle to produce photorealistic details and physically-plausible dynamic range in the clipped areas. We introduce LEDiff, a method that enables a generative model with HDR content generation through latent space fusion inspired by image-space exposure fusion techniques. It also functions as an LDR-to-HDR converter, expanding the dynamic range of existing low-dynamic range images. Our approach uses a small HDR dataset to enable a pretrained diffusion model to recover detail and dynamic range in clipped highlights and shadows. LEDiff brings HDR capabilities to existing generative models and converts any LDR image to HDR, creating photorealistic HDR outputs for image generation, image-based lighting (HDR environment map generation), and photographic effects such as depth of field simulation, where linear HDR data is essential for realistic quality.