GMODiff: One-Step Gain Map Refinement with Diffusion Priors for Efficient HDR Reconstruction

Pre-trained Latent Diffusion Models (LDMs) have recentlyshown strong perceptual priors for low-level vision tasks, making thema promising direction for multi-exposure High Dynamic Range (HDR)reconstruction. However, directly applying LDMs to HDR remains chal-lenging due to: (1) limited dynamic-range representation caused by 8-bitlatent compression, (2) high inference cost from multi-step denoising,and (3) content hallucination inherent to generative nature. To addressthese challenges, we introduce GMODiff, a gain map-driven one-stepdiff usion framework for multi-exposure HDR reconstruction. Instead ofreconstructing full HDR content, we reformulate HDR reconstructionas a degradation-aware Gain Map (GM) refinement problem, where theGM encodes the extended dynamic range while retaining the same bitdepth as LDR images. We initialize the denoising process from an in-formative regression-based estimate rather than pure noise, allowing themodel to generate high-quality GMs in a single denoising step. Further-more, recognizing that regression-based models excel in content fidelitywhile LDMs favor perceptual quality, we leverage regression priors toguide both the denoising process and latent decoding of the LDM, sup-pressing hallucinations while preserving structural accuracy. Extensiveexperiments demonstrate that our GMODiff performs favorably againstseveral state-of-the-art methods and is 100× faster than previous LDM-based methods.