  In this paper, we focus on Exposure Fusion (EF) [ExposFusi2] for dynamic
scenes. The task is to fuse multiple images obtained by exposure bracketing to
create an image which comprises a high level of details. Typically, such images
are not possible to obtain directly from a camera due to hardware limitations,
e.g., a limited dynamic range of the sensor. A major problem of such tasks is
that the images may not be spatially aligned due to scene motion or camera
motion. It is known that the required alignment by image registration problems
is ill-posed. In this case, the images to be aligned vary in their intensity
range, which makes the problem even more difficult.
  To address the mentioned problems, we propose an end-to-end
\emph{Convolutional Neural Network} (CNN) based approach to learn to estimate
exposure fusion from $2$ and $3$ Low Dynamic Range (LDR) images depicting
different scene contents. To the best of our knowledge, no efficient and robust
CNN-based end-to-end approach can be found in the literature for this kind of
problem. The idea is to create a dataset with perfectly aligned LDR images to
obtain ground-truth exposure fusion images. At the same time, we obtain
additional LDR images with some motion, having the same exposure fusion
ground-truth as the perfectly aligned LDR images. This way, we can train an
end-to-end CNN having misaligned LDR input images, but with a proper ground
truth exposure fusion image. We propose a specific CNN-architecture to solve
this problem. In various experiments, we show that the proposed approach yields
excellent results.
