FD-HDRMamba: Frequency-Decoupled Mamba for Multi-Exposure HDR Reconstruction

Abstract

Multi-exposure high dynamic range (HDR) imaging reconstructs scenes with large illumination variations by fusing multiple low dynamic range images, but large exposure gaps and scene motion often lead to ghosting artifacts, luminance inconsistency, detail degradation, and frequency imbalance. To address these challenges, we propose FD-HDRMamba, a frequency-decoupled HDR reconstruction framework that separately models global low-frequency structures and local high-frequency details. The proposed method first performs implicit feature-level alignment to reduce exposure and motion discrepancies, and then decomposes aligned features into frequency components. The low-frequency branch uses Mamba and a low-frequency-aware FFN to capture long-range dependencies and maintain global luminance consistency, while the high-frequency branch adopts residual feature distillation to enhance textures and structural details. Experiments on benchmark datasets show that FD-HDRMamba achieves competitive or superior performance in both quantitative metrics and visual quality, validating the effectiveness of frequency-decoupled HDR reconstruction.