  Single-image dehazing is a challenging problem due to its ill-posed nature.
Existing methods rely on a suboptimal two-step approach, where an intermediate
product like a depth map is estimated, based on which the haze-free image is
subsequently generated using an artificial prior formula. In this paper, we
propose a light dual-task Neural Network called LDTNet that restores the
haze-free image in one shot. We use transmission map estimation as an auxiliary
task to assist the main task, haze removal, in feature extraction and to
enhance the generalization of the network. In LDTNet, the haze-free image and
the transmission map are produced simultaneously. As a result, the artificial
prior is reduced to the smallest extent. Extensive experiments demonstrate that
our algorithm achieves superior performance against the state-of-the-art
methods on both synthetic and real-world images.
