  Avoiding the introduction of ghosts when synthesising LDR images as high
dynamic range (HDR) images is a challenging task. Convolutional neural networks
(CNNs) are effective for HDR ghost removal in general, but are challenging to
deal with the LDR images if there are large movements or
oversaturation/undersaturation. Existing dual-branch methods combining CNN and
Transformer omit part of the information from non-reference images, while the
features extracted by the CNN-based branch are bound to the kernel size with
small receptive field, which are detrimental to the deblurring and the recovery
of oversaturated/undersaturated regions. In this paper, we propose a novel
hierarchical dual Transformer method for ghost-free HDR (HDT-HDR) images
generation, which extracts global features and local features simultaneously.
First, we use a CNN-based head with spatial attention mechanisms to extract
features from all the LDR images. Second, the LDR features are delivered to the
Hierarchical Dual Transformer (HDT). In each Dual Transformer (DT), the global
features are extracted by the window-based Transformer, while the local details
are extracted using the channel attention mechanism with deformable CNNs.
Finally, the ghost free HDR image is obtained by dimensional mapping on the HDT
output. Abundant experiments demonstrate that our HDT-HDR achieves the
state-of-the-art performance among existing HDR ghost removal methods.
