  Although remarkable progress has been made, existing methods for enhancing
underexposed photos tend to produce visually unpleasing results due to the
existence of visual artifacts (e.g., color distortion, loss of details and
uneven exposure). We observed that this is because they fail to ensure the
perceptual consistency of visual information between the source underexposed
image and its enhanced output. To obtain high-quality results free of these
artifacts, we present a novel underexposed photo enhancement approach that is
able to maintain the perceptual consistency. We achieve this by proposing an
effective criterion, referred to as perceptually bidirectional similarity,
which explicitly describes how to ensure the perceptual consistency.
Particularly, we adopt the Retinex theory and cast the enhancement problem as a
constrained illumination estimation optimization, where we formulate
perceptually bidirectional similarity as constraints on illumination and solve
for the illumination which can recover the desired artifact-free enhancement
results. In addition, we describe a video enhancement framework that adopts the
presented illumination estimation for handling underexposed videos. To this
end, a probabilistic approach is introduced to propagate illuminations of
sampled keyframes to the entire video by tackling a Bayesian Maximum A
Posteriori problem. Extensive experiments demonstrate the superiority of our
method over the state-of-the-art methods.
