Super-resolution Reconstruction of Remote Sensing Image Based on Swin Transformer
Due to the uncertainty of objects in remote sensing images and significant differences in feature information between different images,existing super-resolution methods yield poor reconstruction results.Therefore,this study proposes an NG-MAT model that combines the Swin Transformer and the N-gram model to achieve super-resolution of remote sensing images.Firstly,multiple attention modules are connected in parallel on the branch of the original Transformer to extract global feature information for activating more pixels.Secondly,the N-gram model from natural language processing is applied to the field of image processing,utilizing a trigram N-gram model to enhance information interaction between windows.The proposed method achieves peak signal-to-noise ratios of 34.68 dB,31.03 dB,and 28.99 dB at amplification factors of 2,3,and 4,respectively,and structural similarity indices of 0.926 6,0.844 4,and 0.773 4 at the same amplification factors on the selected dataset.Experimental results demonstrate that the proposed method outperforms other similar methods in various metrics.
万方数据
维普
