A Flexible Zero-Shot Approach to Tone Mapping via Structure-Preserving Diffusion Models

With the prevalence of high dynamic range (HDR) imaging, tone mapping techniques, which convert HDR images to high-quality standard dynamic range (SDR) images for display, have become increasingly important. However, obtaining paired HDR and high-quality SDR images is almost impossible, posing challenges to learning-based tone mapping methods. To address this issue, we propose a zero-shot tone mapping framework without requiring any HDR training samples. Our approach decomposes images into two components: structural information and tonal information. A diffusion-based mapping model taking the structural information as input is first trained in the high-quality SDR domain, then transferred to the HDR domain that has less readily available training data for inference, leveraging the equivalent distribution of the structural information across both domains. To preserve the original image’s structure, we modify the reverse sampling process and explicitly incorporate the original structural information into the intermediate results. To improve the image details, we introduce a dual-control network, enabling different conditional inputs to control different scales of the output. Additionally, we devise a flexible tone adjustment strategy, with a bunch of novel loss functions to modify the trained score function dynamically during reverse sampling, allowing users to customize the style of the generated image according to their preference during testing. Initially designed for tone mapping, our model can be applied to various tasks including image fusion, exposure correction, dehazing, etc., without retraining. Experimental results demonstrate that our approach surpasses previous state-of-the-art methods, indicating that it can serve as an effective, flexible and versatile solution to various tone-mapping tasks. Source code is available at https://github.com/ZSDM-HDR/Zero-Shot-Diffusion-HDR.