Specifically, the global branch employs a window-based Transformer encoder to model long-range object movements and intensity variations to solve ghosting. The CA-ViT is designed as a dual-branch architecture, which can jointly capture both global and local dependencies. In this paper, we propose a novel Context-Aware Vision Transformer (CA-ViT) for ghost-free high dynamic range imaging. 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. High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images with realistic details. 2022.07.04 Our paper has been accepted by ECCV 2022.2022.08.26 The PyTorch implementation of our paper is now available.The MegEngine version is available at HDR-Transformer-MegEngine. This is the official PyTorch implementation of our ECCV2022 paper: Ghost-free High Dynamic Range Imaging with Context-aware Transformer ( HDR-Transformer). Ghost-free High Dynamic Range Imaging with Context-aware Transformerīy Zhen Liu 1, Yinglong Wang 2, Bing Zeng 3 and Shuaicheng Liu 3,1*ġMegvii Technology, 2Noah’s Ark Lab, Huawei Technologies, 3University of Electronic Science and Technology of China
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