Abstract:
Haze removal from a single image is a challenging
task. Estimation of accurate scene transmission map (TrMap)
is the key to reconstruct the haze-free scene. In this paper,
we propose a convolutional neural network based architecture
to estimate the TrMap of the hazy scene. The proposed network
takes the hazy image as an input and extracts the haze relevant
features using proposed RNet and YNet through RGB and
YCbCr color spaces respectively and generates two TrMaps.
Further, we propose a novel TrMap fusion network (FNet) to
integrate two TrMaPs and estimate robust TrMap for the hazy
scene. To analyze the robustness of FNet, we tested it on combinations of TrMaps obtained from existing state-of-the-art methods.
Performance evaluation of the proposed approach has been
carried out using the structural similarity index, mean square
error and peak signal to noise ratio. We conduct experiments on
five datasets namely: D-HAZY, Imagenet, Indoor SOTS, HazeRD
and set of real-world hazy images. Performance analysis shows
that the proposed approach outperforms the existing state-of-theart methods for single image dehazing. Further, we extended our
work to address high-level vision task such as object detection in
hazy scenes. It is observed that there is a significant improvement
in accurate object detection in hazy scenes using proposed
approach.