Guided LDR Generation for Single-Image HDR Reconstruction Using Exposure Priors From Multi-Exposure Images

Abstract

Single-image high dynamic range (HDR) reconstruction remains challenging due to exposure-induced degradations. Several recent methods attempt to synthesize multi-exposure low dynamic range (LDR) images from a single input and fuse them to recover missing content. However, they primarily rely on implicit statistical priors learned from data and do not explicitly extract or constrain exposure-related degradation patterns, which limits their ability to accurately restore detail and color under extreme lighting conditions. To address this, we present a two-stage framework, Degradation-Guided Single-Image HDR (DG-SHDR), which formulates HDR reconstruction as a pipeline of degradation modeling and degradation-aware image generation. Our framework consists of two core components: 1) a degradation estimation network (DEN) that learns the image exposure prior from medium-low and medium-high exposure image pairs. 2) a U-shaped degradation-guided reconstruction network (DGR) intended to integrate the exposure prior and restore more accurate low- and high-exposure images. These generated images are subsequently fused to produce a high-quality HDR output. Extensive quantitative and qualitative experimental results demonstrate that our method excels in detail, brightness, and color restoration, significantly improving the overall performance of single-image HDR reconstruction. The codes and models will be available at https://github.com/gguoshun/DG-SHDR