  Recovering ghost-free High Dynamic Range (HDR) images from multiple Low
Dynamic Range (LDR) images becomes challenging when the LDR images exhibit
saturation and significant motion. Recent Diffusion Models (DMs) have been
introduced in HDR imaging field, demonstrating promising performance,
particularly in achieving visually perceptible results compared to previous
DNN-based methods. However, DMs require extensive iterations with large models
to estimate entire images, resulting in inefficiency that hinders their
practical application. To address this challenge, we propose the Low-Frequency
aware Diffusion (LF-Diff) model for ghost-free HDR imaging. The key idea of
LF-Diff is implementing the DMs in a highly compacted latent space and
integrating it into a regression-based model to enhance the details of
reconstructed images. Specifically, as low-frequency information is closely
related to human visual perception we propose to utilize DMs to create compact
low-frequency priors for the reconstruction process. In addition, to take full
advantage of the above low-frequency priors, the Dynamic HDR Reconstruction
Network (DHRNet) is carried out in a regression-based manner to obtain final
HDR images. Extensive experiments conducted on synthetic and real-world
benchmark datasets demonstrate that our LF-Diff performs favorably against
several state-of-the-art methods and is 10$\times$ faster than previous
DM-based methods.
