  Modern cameras have limited dynamic ranges and often produce images with
saturated or dark regions using a single exposure. Although the problem could
be addressed by taking multiple images with different exposures, exposure
fusion methods need to deal with ghosting artifacts and detail loss caused by
camera motion or moving objects. This paper proposes a deep network for
exposure fusion. For reducing the potential ghosting problem, our network only
takes two images, an underexposed image and an overexposed one. Our network
integrates together homography estimation for compensating camera motion,
attention mechanism for correcting remaining misalignment and moving pixels,
and adversarial learning for alleviating other remaining artifacts. Experiments
on real-world photos taken using handheld mobile phones show that the proposed
method can generate high-quality images with faithful detail and vivid color
rendition in both dark and bright areas.
