HL-HDR: Multi-Exposure High Dynamic Range Reconstruction with High-Low Frequency Decomposition

Generating high-quality High Dynamic Range (HDR) images in dynamic scenes is particularly challenging. Recent Transformer have been introduced in HDR imaging, demonstrating promising performance, particularly in scenarios involving large-scale motion compared to previous CNN-based methods. However, Transformer-based methods face hurdles capturing local details and come with high computational complexity, hindering further progress. In this paper, inspired by the distinct characteristics of high and low-frequency in image patterns, we propose a Frequency Decomposition Processing Block (FDPB) for ghost-free HDR imaging. In the image reconstruction process, FDPB decouples features into resolution-invariant high-frequency features and resolution-reduced low-frequency features to separately address local and global information. Specifically, considering the characteristics of different frequencies, for the high-frequency components, we design a Local Feature Extractor (LFE) based on CNN to extract local feature maps. Meanwhile, for the low-frequency components, we propose a Global Feature Extractor (GFE) that learns long-range dependencies through carefully designed Transformer modules. Importantly, the downscaled low-frequency features exploit Transformer’s remote learning capabilities while substantially reducing self-attention computational costs. By incorporating the FDPB as basic components, we further build a Low/High-Frequency Aware Network (HL-HDR), a hierarchical network to reconstruct high-quality ghost-free HDR images. Extensive experiments on four public datasets confirm the superior performance of the proposed method, both in terms of quantitative and qualitative evaluations.