UMTransNet:Image stitching and localization method combining U-Net and multi-scale perception Transformer
Most of the current deep learning based image stitching and localization methods are primarily focused on deep-level features with limited receptive fields,thereby overlooking shallow-level features,which adversely affects the accuracy of image stitching and localization.In view of the above,a novel image stitching and localization network UMTransNet which combines an improved U-Net architecture with a multi-scale multi-view Transformer is proposed.The encoder of the U-Net model is enhanced,and the maximum pooling layer of the encoder is replaced with convolutional layers to prevent the loss of shallow-level features.Additionally,the multi-scale multi-view Transformer is embedded into the skip connections of the U-Net,which facilitates the effective fusion of the output features of the Transformer and the upsampled features of the U-Net,so as to achieve a balance between deep-level and shallow-level features,thereby enhancing the accuracy of image stitching and localization.The results of visualization detection graph show that the proposed methed is more excellent in locating stitched tampered regions.
digital image forensicsimage stitching localizationU-Netmulti-scale perceptionself-attention mechanismcross-attention mechanism
万方数据
维普
