  High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR
images with realistic details. Restricted by the locality of the receptive
field, existing CNN-based methods are typically prone to producing ghosting
artifacts and intensity distortions in the presence of large motion and severe
saturation. In this paper, we propose a novel Context-Aware Vision Transformer
(CA-ViT) for ghost-free high dynamic range imaging. The CA-ViT is designed as a
dual-branch architecture, which can jointly capture both global and local
dependencies. Specifically, the global branch employs a window-based
Transformer encoder to model long-range object movements and intensity
variations to solve ghosting. For the local branch, we design a local context
extractor (LCE) to capture short-range image features and use the channel
attention mechanism to select informative local details across the extracted
features to complement the global branch. By incorporating the CA-ViT as basic
components, we further build the HDR-Transformer, a hierarchical network to
reconstruct high-quality ghost-free HDR images. Extensive experiments on three
benchmark datasets show that our approach outperforms state-of-the-art methods
qualitatively and quantitatively with considerably reduced computational
budgets. Codes are available at
https://github.com/megvii-research/HDR-Transformer
