基于注意力特征融合稠密网络的图像去雾算法

目前主流图像去雾算法输出的结果图像存在颜色失真、边缘模糊的问题.为改善上述问题，提出一种基于深度学习的图像去雾算法，所提算法由两个模块构成:注意力特征融合模块和雾霾模型参数估计模块.注意力特征融合模块用于充分提取雾霾图像的颜色、边缘特征；基于稠密连接空洞卷积自编码器的雾霾模型参数估计模块用于估计雾霾模型的参数，改善网络退化的问题.在浓雾图像、薄雾图像数据集上的实验表明，本文提出的算法有效地实现了图像去雾，与主流的图像去雾算法相比具有更高的结构相似性(SSIM)，更低的均方误差(mean-square error，MSE)和边缘误差e_○edge.

Image Dehazing Algorithm Based on Attentional Feature Fusion and Dense Network

There are problems of distorted colors and blurred edges in the results of the state-of-the-art image dehazing algorithms. For solving the problems，an image dehazing algorithm based on deep learning is proposed. The proposed algorithm consists of two modules: attentional feature fusion module and haze model parameter estimation module. Attentional feature fusion module is designed to extract the color and edge features of hazy images sufficiently. Haze model parameter estimation module based on densely connected dilated convolution auto encoder is used to estimate the parameter of haze model and deal with the network degeneration in image dehazing. Experiments on images with thin haze and thick haze show that the proposed algorithm performs well on image dehazing， and the proposed dehazing algorithm has higher structural similarity (SSIM)， lower mean-square error (MSE)， lower edge error e_○edge than the state-of-the-art image dehazing algorithms.