A novel image-dehazing network with a parallel attention block.

• An end-to-end network with a parallel spatial/channel-wise attention block is proposed for image dehazing. • The spatial attention module and the channelwise attention module are combined in parallel for obtaining powerful features. • The accuracy of the dehazing results is improved by using one parallel spatial/channel-wise attention block in the network. Image dehazing is a very important pre-processing step to many computer vision tasks such as object recognition and tracking. However, it is a challenging problem because the physical parameters of imaging, e.g. the depth information of scene pixels and the attenuation model, are usually unknown. Based on a physical model, different methods have been proposed to recover these parameters. Existing convolutional neural networks (CNNs) based methods try to solve the image dehazing problem using an end-to-end network to learn a direct mapping between a hazy image and its corresponding clear image. But the representational ability, spatial variant ability and dehazing capability of these network models are hindered by treating all the spatial and channel-wise features indiscriminately. Hence, we propose an end-to-end dehazing network with a parallel spatial/channel-wise attention block for capturing more informative spatial and channel-wise features respectively. Specifically, based on the encoder-decoder framework with a pyramid pooling operation, a novel parallel spatial/channel-wise attention block is proposed and applied to the end of the encoder for guiding the decoder to reconstruct better clear images. In the spatial/channel-wise attention block, the spatial attention module and the channel-wise attention module are connected in parallel, where the spatial attention module highlights important spatial positions of features. Meanwhile, the channel-wise module exploits inter-dependencies among the channel-wise features. Extensive experiments demonstrate that our network with a parallel spatial /channel-wise attention block can achieve better accuracy and visual results over state-of-the-art methods. [ABSTRACT FROM AUTHOR]