Single image haze removal is a challenging ill-posed problem. Existing
methods use various constraints/priors to get plausible dehazing solutions. The
key to achieve haze removal is to estimate a medium transmission map for an
input hazy image. In this paper, we propose a trainable end-to-end system
called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy
image as input, and outputs its medium transmission map that is subsequently
used to recover a haze-free image via atmospheric scattering model. DehazeNet
adopts Convolutional Neural Networks (CNN) based deep architecture, whose
layers are specially designed to embody the established assumptions/priors in
image dehazing. Specifically, layers of Maxout units are used for feature
extraction, which can generate almost all haze-relevant features. We also
propose a novel nonlinear activation function in DehazeNet, called Bilateral
Rectified Linear Unit (BReLU), which is able to improve the quality of
recovered haze-free image. We establish connections between components of the
proposed DehazeNet and those used in existing methods. Experiments on benchmark
images show that DehazeNet achieves superior performance over existing methods,
yet keeps efficient and easy to use.

本文提出一种称为 DehazeNet 的端到端可训练系统，用于中介介质透射图的估计，以便于去除输入图像中的雾霾，并通过大气散射模型恢复无雾图像。DehazeNet 采用卷积神经网络（CNN）的深层架构，其层特别设计为体现图像去雾中已建立的假设 / 先验，包括使用 Maxout 单元的特征提取层和双边整流线性单元的非线性激活函数，实验表明 DehazeNet 具有优越的性能。