Single image defogging is a classical and challenging problem in computer
vision. Existing methods towards this problem mainly include handcrafted priors
based methods that rely on the use of the atmospheric degradation model and
learning based approaches that require paired fog-fogfree training example
images. In practice, however, prior-based methods are prone to failure due to
their own limitations and paired training data are extremely difficult to
acquire. Inspired by the principle of CycleGAN network, we have developed an
end-to-end learning system that uses unpaired fog and fogfree training images,
adversarial discriminators and cycle consistency losses to automatically
construct a fog removal system. Similar to CycleGAN, our system has two
transformation paths; one maps fog images to a fogfree image domain and the
other maps fogfree images to a fog image domain. Instead of one stage mapping,
our system uses a two stage mapping strategy in each transformation path to
enhance the effectiveness of fog removal. Furthermore, we make explicit use of
prior knowledge in the networks by embedding the atmospheric degradation
principle and a sky prior for mapping fogfree images to the fog images domain.
In addition, we also contribute the first real world nature fog-fogfree image
dataset for defogging research. Our multiple real fog images dataset (MRFID)
contains images of 200 natural outdoor scenes. For each scene, there are one
clear image and corresponding four foggy images of different fog densities
manually selected from a sequence of images taken by a fixed camera over the
course of one year. Qualitative and quantitative comparison against several
state-of-the-art methods on both synthetic and real world images demonstrate
that our approach is effective and performs favorably for recovering a clear
image from a foggy image.

本研究提出了一种基于 CycleGAN 网络的端到端学习系统，借助对抗鉴别器和循环一致性损失来自动构建一个除雾系统，并且为此贡献了第一个真实世界的自然雾与雾不含的图像数据集。实验结果表明我们的方法可以有效地从雾图像中恢复出明亮的图像。

基于增强循环一致性对抗网络的端到端单图像去雾

End-to-End Single Image Fog Removal using Enhanced Cycle Consistent  Adversarial Networks

In this paper, we propose a structured feature learning framework to reason
the correlations among body joints at the feature level in human pose
estimation. Different from existing approaches of modelling structures on score
maps or predicted labels, feature maps preserve substantially richer
descriptions of body joints. The relationships between feature maps of joints
are captured with the introduced geometrical transform kernels, which can be
easily implemented with a convolution layer. Features and their relationships
are jointly learned in an end-to-end learning system. A bi-directional tree
structured model is proposed, so that the feature channels at a body joint can
well receive information from other joints. The proposed framework improves
feature learning substantially. With very simple post processing, it reaches
the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of
learning features at each joint separately with ConvNet, the mean PCP has been
improved by 18% on FLIC. The code is released to the public.

该论文提出了一种结构化特征学习框架，旨在通过人体姿态估计中的特征级别推理身体关节之间的相关性，通过引入几何变换内核捕捉关节特征地图之间的关系，提高特征学习。