  Automatically selecting exposure bracketing (images exposed differently) is
important to obtain a high dynamic range image by using multi-exposure fusion.
Unlike previous methods that have many restrictions such as requiring camera
response function, sensor noise model, and a stream of preview images with
different exposures (not accessible in some scenarios e.g. some mobile
applications), we propose a novel deep neural network to automatically select
exposure bracketing, named EBSNet, which is sufficiently flexible without
having the above restrictions. EBSNet is formulated as a reinforced agent that
is trained by maximizing rewards provided by a multi-exposure fusion network
(MEFNet). By utilizing the illumination and semantic information extracted from
just a single auto-exposure preview image, EBSNet can select an optimal
exposure bracketing for multi-exposure fusion. EBSNet and MEFNet can be jointly
trained to produce favorable results against recent state-of-the-art
approaches. To facilitate future research, we provide a new benchmark dataset
for multi-exposure selection and fusion.
