  High Dynamic Range (HDR) imaging aims to generate an artifact-free HDR image
with realistic details by fusing multi-exposure Low Dynamic Range (LDR) images.
Caused by large motion and severe under-/over-exposure among input LDR images,
HDR imaging suffers from ghosting artifacts and fusion distortions. To address
these critical issues, we propose an HDR Transformer Deformation Convolution
(HDRTransDC) network to generate high-quality HDR images, which consists of the
Transformer Deformable Convolution Alignment Module (TDCAM) and the Dynamic
Weight Fusion Block (DWFB). To solve the ghosting artifacts, the proposed TDCAM
extracts long-distance content similar to the reference feature in the entire
non-reference features, which can accurately remove misalignment and fill the
content occluded by moving objects. For the purpose of eliminating fusion
distortions, we propose DWFB to spatially adaptively select useful information
across frames to effectively fuse multi-exposed features. Extensive experiments
show that our method quantitatively and qualitatively achieves state-of-the-art
performance.
