从全局到局部: 双注意力融合去雾网络

In terms of the problem that the existing dehazing methods based on the convolution neural network employs attention only from a single perspective, causing difficulties in generating a clear image with vivid details and the propensity to give rise to color distortion, this paper proposes a global and local attention fusion image dehazing network is proposed, in order to obtain a dehazing image with normal definition and no color distortion. The input haze image is first divided into two parts in the channel dimension by using the channel attention. One part is sent into the channel pixel attention channel to extract local features, and the other part is sent into the Transformer channel to learn global features. Then, the pixel attention is used to fuse the features learned by the two channels, and the above modules are combined as basic units into a multi-level U-shaped dehazing network, residual connection is added to alleviate the loss of detail information caused by upper and lower sampling, and finally, a Transformer module is added at the bottom of the network to learn global information. The effectiveness of the method proposed is tested on several publicly available dehazing image data sets including RESIDE SOTS Indoor and RESIDE SOTS Outdoor. The results show that compared with the classical dehazing method, the image generated by the method proposed is more detailed and has the least color distortion. On the RESIDE SOTS Outdoor data set, the PSNR is 1. 16dB higher than that of the classical FFA-Net, and 3. 68dB higher than that of the GridDchazeNet. The global and local attention fusion method proposed in this paper can effectively remove haze and improve the contrast and clarity of the image; the designed multi-level U-shaped dehazing network and residual connection structure can alleviate the loss of details and improve the dehazing effect, so that clear images are obtained.