High Dynamic Range Imaging via Spatial-Frequency Interaction

High Dynamic Range (HDR) imaging aims to reconstruct scenes with a wide range of luminance by fusing multi-exposure Low Dynamic Range (LDR) images. In dynamic scenes with pronounced foreground motion or camera jitter, especially under challenging conditions including extremely low or high luminance, widespread saturation, and substantial motion, existing approaches often encounter ghosting artifacts, spatial misalignment, and degradation of fine structural details. Traditional techniques based on handcrafted priors struggle to generalize to complex motion patterns, while most deep learning-based methods operate exclusively in the spatial domain, limiting their ability to capture global contextual cues and restore high-frequency structures that are better represented in the frequency domain. To address these challenges, we introduce a Dual-Domain Parallel Fusion Network with Prompt Refinement (DDPF-PR), which jointly leverages spatial and frequency-domain features for enhanced HDR reconstruction. Specifically, the proposed framework consists of a Bi-Domain Interaction Module(BDIM), which integrates spatial features for local detail and frequency features for global structure to suppress ghosting artifacts caused by motion. In addition, a Prompt Refinement Module(PRM) is designed to recover fine details in degraded regions such as saturated or misaligned areas by adaptively generating structural cues. Extensive experiments demonstrate that DDPF-PR consistently outperforms state-of-the-art methods across multiple benchmarks in both qualitative and quantitative evaluations. The code will be made publicly available.