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. © 2023 Science Press. All rights reserved.