  Presence of haze in images obscures underlying information, which is
undesirable in applications requiring accurate environment information. To
recover such an image, a dehazing algorithm should localize and recover
affected regions while ensuring consistency between recovered and its
neighboring regions. However owing to fixed receptive field of convolutional
kernels and non uniform haze distribution, assuring consistency between regions
is difficult. In this paper, we utilize an encoder-decoder based network
architecture to perform the task of dehazing and integrate an spatially aware
channel attention mechanism to enhance features of interest beyond the
receptive field of traditional conventional kernels. To ensure performance
consistency across diverse range of haze densities, we utilize greedy localized
data augmentation mechanism. Synthetic datasets are typically used to ensure a
large amount of paired training samples, however the methodology to generate
such samples introduces a gap between them and real images while accounting for
only uniform haze distribution and overlooking more realistic scenario of
non-uniform haze distribution resulting in inferior dehazing performance when
evaluated on real datasets. Despite this, the abundance of paired samples
within synthetic datasets cannot be ignored. Thus to ensure performance
consistency across diverse datasets, we train the proposed network within an
adversarial prior-guided framework that relies on a generated image along with
its low and high frequency components to determine if properties of dehazed
images matches those of ground truth. We preform extensive experiments to
validate the dehazing and domain invariance performance of proposed framework
across diverse domains and report state-of-the-art (SoTA) results.
