雾霾的存在会严重影响户外图像的质量，进而会影响到某些系统的可用性，例如电子警察等。雾霾天气在我国呈现多发态势，如何快速去除雾霾对相关系统的不利影响成为亟待解决的问题。在本项目中，我们将围绕两个关键问题展开研究：一是如何设计算法来准确评估图像中的雾霾程度，二是如何设计去雾霾算法来从有雾霾的图像中复原出高质量清晰图像。为了解决以上两个问题，我们提出了以深度神经网络为基础的解决方案：1）构建用于图像雾霾程度度量研究的大规模数据集；2）提出基于深度神经网络和视觉显著性模型的图像雾霾程度度量算法；3）提出利用深度神经网络和多尺度邻域策略来解决基于单张图像的场景深度估计问题，进而模拟生成带雾霾的图像样本；4）提出基于深度神经网络和雾霾程度度量模型的图像去雾霾算法。基于这套解决方案，我们可以更加高效准确地预测图像中的雾霾程度和复原雾霾条件下采集的图像。本项目的研究成果将具有广阔的应用前景。

The presence of haze will seriously affect the quality of outdoor images, thereby affecting the availability of some related systems, such as the electronic police, etc. In China, foggy or hazy days happen with high frequency. Thus, how to quickly remove the negative effects of haze on related systems has become an urgent problem. In view of this, in this project, we mainly focus on two issues brought by haze to the imaging systems. The first issue is how to design algorithms to accurately measure the density of haze in an image. The second one is how to design a haze removal algorithm to recover a high quality clear image from the original hazy one. In order to solve the above-mentioned two problems, we have proposed novel solutions, which are primarily based on the theory of deep neural network. Specifically, our research work and the innovation of this project mainly include the following aspects. 1) We will construct a large-scale dataset, which will be used to design and test models for estimating the haze density in an image. 2) We propose a novel algorithm for estimating the haze density in an image based on the deep neural network and the computational visual saliency model. 3) In order to estimate the depth map of a scene from a single image, we propose an approach based on a multi-channel deep neural network taking multi-scale contexts as input; then, based on this fruit, we will study how to simulate hazy images from the original clear ones. 4) We propose a novel haze removal algorithm based on the deep neural network and the haze density estimation model. Based on the proposed solutions, the haze density in an image can be predicted efficiently and accurately; high-quality clear images can be recovered automatically and efficiently from those captured in hazy days. The research output of this project will have broad application prospects.