Semantic Aware Diffusion Inverse Tone Mapping

Capturing the full luminance range of real-world scenes exceeds the capabilities of most digital cameras, often resulting in detail loss, particularly in bright regions. Inverse tone mapping aims to reconstruct High Dynamic Range (HDR) images from Standard Dynamic Range (SDR) inputs, but typically fails to recover clipped details. This paper presents a novel semantic-aware diffusion-based inpainting approach for inverse tone mapping1. Our method introduces two key contributions: (1) a semantic graph-guided diffusion process to inpaint saturated SDR regions, and (2) a principled HDR lifting formulation inspired by traditional HDR bracketing, designed to complement generative inpainting techniques. Experiments demonstrate that our approach outperforms existing methods both quantitatively and qualitatively across multiple datasets.