Abstract
This paper presents a new neural network for enhancing underexposed photos. Instead of directly learning an
image-to-image mapping as previous work, we introduce
intermediate illumination in our network to associate the
input with expected enhancement result, which augments
the network’s capability to learn complex photographic adjustment from expert-retouched input/output image pairs.
Based on this model, we formulate a loss function that
adopts constraints and priors on the illumination, prepare
a new dataset of 3,000 underexposed image pairs, and train
the network to effectively learn a rich variety of adjustment
for diverse lighting conditions. By these means, our network is able to recover clear details, distinct contrast, and
natural color in the enhancement results. We perform extensive experiments on the benchmark MIT-Adobe FiveK
dataset and our new dataset, and show that our network
is effective to deal with previously challenging images.