基于内容特征和风格特征融合的单幅图像去雾网络

基于深度学习的方法在去雾领域已经取得了很大进展,但仍然存在去雾不彻底和颜色失真等问题.针对这些问题,本文提出一种基于内容特征和风格特征相融合的单幅图像去雾网络.所提网络包括特征提取、特征融合和图像复原三个子网络,其中特征提取网络包括内容特征提取模块和风格特征提取模块,分别用于学习图像内容和图像风格以实现去雾的同时可较好地保持原始图像的色彩特征.在特征融合子网络中,引入注意力机制对内容特征提取模块输出的特征图进行通道加权实现对图像主要特征的学习,并将加权后的内容特征图与风格特征图通过卷积操作相融合.最后,图像复原模块对融合后的特征图进行非线性映射得到去雾图像.与已有方法相比,所提网络对合成图像和真实图像均可取得理想的去雾结果,同时可有效避免去雾后的颜色失真问题.

Content Feature and Style Feature Fusion Network for Single Image Dehazing

Although recent research has shown the potential of using deep learning to accomplish single image dehazing, existing methods still have some problems, such as poor visibility and color distortion. To overcome these shortcomings, we present a content feature and style feature fusion network for single image dehazing. The dehazing network consists of three parts: Feature extraction sub-network, feature fusion sub-network and image restoration sub-network. The feature extraction sub-network consists of a content feature extraction module and a style feature extraction module, which can learn image content and image style respectively to achieve pleasing dehazing results and maintain original color characteristics simultaneously. In the feature fusion sub-network, the channel-wise attention mechanism is adopted to weight the feature maps generated from the content feature extraction module in order to learn the most important features of the image, and then the weighted content feature map and style feature map are fused by convolution operation. Finally, a non-linear mapping is performed to recover the dehazed image. Compared with the existing approaches, the proposed network can obtain superior results on synthetic and real images, and can avoid the color distortion effectively.