<p>High dynamic range (HDR) imaging typically merges several low dynamic range (LDR) images to generate a ghost-free HDR image with rich details in both bright and dark regions. Previous methods primarily focused on aligning LDR frames and deghosting to produce ghost-free HDR images. However, they fail to effectively reconstruct fine-grained details from multiple LDR frames and tend to produce ghosting artifacts or distortion in regions with significant motion or over-exposure. To this end, we propose a Cross-Frame Detail Compensation Network for Ghost-free HDR imaging, called CDCNet, to reconstruct high-quality HDR images with rich multi-frame details while significantly reducing ghosting artifacts. Our CDCNet consists of a detail compensation network (DCN) for integrating fine-grained multi-frame details and a feature refinement network (FRN) for deghosting. Specifically, the DCN utilizes a dual-branch alignment module, comprising a Detail Compensation module and a Global Alignment module, to align LDR features and capture complementary cross-frame details. Next, the FRN includes a spatially-refined deghosting (SRD) block with a dual-branch architecture for spatially-enhanced deghosting. The SRD block suppresses ghosting artifacts exploiting a deformable re-attention Transformer encoder, while refining spatial details with a spatial feature extractor. We evaluate our method on three benchmark HDR datasets. Extensive experiments validate the effectiveness of our method in generating realistic and detailed HDR images, both quantitatively and qualitatively, outperforming existing methods.</p>