  High dynamic range (HDR) imaging from multiple low dynamic range (LDR) images
has been suffering from ghosting artifacts caused by scene and objects motion.
Existing methods, such as optical flow based and end-to-end deep learning based
solutions, are error-prone either in detail restoration or ghosting artifacts
removal. Comprehensive empirical evidence shows that ghosting artifacts caused
by large foreground motion are mainly low-frequency signals and the details are
mainly high-frequency signals. In this work, we propose a novel
frequency-guided end-to-end deep neural network (FHDRNet) to conduct HDR fusion
in the frequency domain, and Discrete Wavelet Transform (DWT) is used to
decompose inputs into different frequency bands. The low-frequency signals are
used to avoid specific ghosting artifacts, while the high-frequency signals are
used for preserving details. Using a U-Net as the backbone, we propose two
novel modules: merging module and frequency-guided upsampling module. The
merging module applies the attention mechanism to the low-frequency components
to deal with the ghost caused by large foreground motion. The frequency-guided
upsampling module reconstructs details from multiple frequency-specific
components with rich details. In addition, a new RAW dataset is created for
training and evaluating multi-frame HDR imaging algorithms in the RAW domain.
Extensive experiments are conducted on public datasets and our RAW dataset,
showing that the proposed FHDRNet achieves state-of-the-art performance.
