Multi Scale Progressive Transformer for Image Dehazing
In order to simultaneously recover image details and maintain global information in the dehazed image,a multi scale progressive transformer(MSP-Transformer)is proposed for image dehazing.The MSP-Transformer can effectively extract haze-related features from different scales,and restore clear image in a progressive way,achieving multi-scale learning and fusion of features and images.The proposed MSP-Transformer is divided into an encoding stage,a decoding stage,and a restoration stage.In the encoding stage,a Transformer block-based encoder is used to decompose the input image into different scales.The extrac-ted haze-relevant features from different scales can fully characterize the information loss of the haze image.In the decoding stage,considering that different regions of the haze image have different information loss,this paper designs a feature aggregation module containing a multi-scale attention mechanism in decoder.The multi-scale attention contains channel attention and multi-scale spatial attention,and can fuse the feature information from different scales.The restoration stage contains restoration block and fusion block,firstly,the multi-scale feature fusion restoration block aggregates the haze relevant features from different scales to increase the association between these features,then the aggregated features are used to restore a haze-free image at each scale.Besides,the restored images from each scale are fused by fusion block to obtain the final dehazed result.Qualitative and quantita-tive experiments on both real and synthetic datasets show that the proposed MSP-Transformer has good dehazing performance.Compared with 11 state-of-the-art methods,MSP-Transformer obtains the best PSNR(39.53db)and SSIM(0.9954)on the RE-SIDE dataset,and achieves good visual effect.In addition,the ablation experiments also demonstrate the effectiveness of the pro-posed dehazing method.
万方数据
维普
