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.

本文提出了一种基于编码器 - 解码器网络和空间感知通道注意机制的去雾算法，并引入一种贪婪本地化数据增强机制以确保去除影响后的图像的一致性，为了消除人工合成图像与真实数据集之间的差异，提出了对抗性的先验引导框架来确保性能一致性，并在实验中取得了最先进的效果。