Ghost-Free HDR Imaging via Latent Low-Frequency Priors and Deformable Attention Alignment

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, showing promising performance, particularly in achieving visually perceptible better 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. These priors are integrated into a carefully designed Dynamic HDR Reconstruction Network (DHRNet), which employs a regression-based approach to produce high-quality HDR images. Furthermore, we introduce the Attention-guided Deformable Alignment Module (ADAM) that utilizes correlation-driven feature matching to learn deformable receptive fields for self-attention, enabling efficient pre-alignment of LDR images by focusing on salient regions. Extensive experiments 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.