  Capturing photographs with wrong exposures remains a major source of errors
in camera-based imaging. Exposure problems are categorized as either: (i)
overexposed, where the camera exposure was too long, resulting in bright and
washed-out image regions, or (ii) underexposed, where the exposure was too
short, resulting in dark regions. Both under- and overexposure greatly reduce
the contrast and visual appeal of an image. Prior work mainly focuses on
underexposed images or general image enhancement. In contrast, our proposed
method targets both over- and underexposure errors in photographs. We formulate
the exposure correction problem as two main sub-problems: (i) color enhancement
and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep
neural network (DNN) model, trainable in an end-to-end manner, that addresses
each sub-problem separately. A key aspect of our solution is a new dataset of
over 24,000 images exhibiting the broadest range of exposure values to date
with a corresponding properly exposed image. Our method achieves results on par
with existing state-of-the-art methods on underexposed images and yields
significant improvements for images suffering from overexposure errors.
