  We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for
Multi-Bracket HDR Imaging. To address the challenges of fusing multiple
brackets from dynamic scenes, we propose an efficient dual branch network that
operates on two different resolutions. The full resolution branch uses a
Deformable Convolutional Block to align features and retain high-frequency
details. A low resolution branch with a Spatial Attention Block aims to attend
wanted areas from the non-reference brackets, and suppress displaced features
that could incur on ghosting artifacts. By using a dual branch approach we are
able to achieve high quality results while constraining the computational
resources required to estimate the HDR results.
