  Mapping Low Dynamic Range (LDR) images with different exposures to High
Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to
ghosting caused by object motion or camera jitting. With the success of Deep
Neural Networks (DNNs), several DNNs-based methods have been proposed to
alleviate ghosting, they cannot generate approving results when motion and
saturation occur. To generate visually pleasing HDR images in various cases, we
propose a hybrid HDR deghosting network, called HyHDRNet, to learn the
complicated relationship between reference and non-reference images. The
proposed HyHDRNet consists of a content alignment subnetwork and a
Transformer-based fusion subnetwork. Specifically, to effectively avoid
ghosting from the source, the content alignment subnetwork uses patch
aggregation and ghost attention to integrate similar content from other
non-reference images with patch level and suppress undesired components with
pixel level. To achieve mutual guidance between patch-level and pixel-level, we
leverage a gating module to sufficiently swap useful information both in
ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image,
the Transformer-based fusion subnetwork uses a Residual Deformable Transformer
Block (RDTB) to adaptively merge information for different exposed regions. We
examined the proposed method on four widely used public HDR image deghosting
datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art
methods both quantitatively and qualitatively, achieving appealing HDR
visualization with unified textures and colors.
