Image denoising using channel attention residual enhanced Swin Transformer Transformers have achieved remarkable results in high-level vision tasks, but their application in low-level computer vision tasks such as image denoising ... Q Dai,X Cheng,L Zhang - 《Multimedia Tools & Applications》 被引量...
16 developed an end-to-end framework termed Automatic Consecutive Context-Perceived Transformer GAN for serial imaging blindly inpainted automatically. Zheng et al.17 proposed an encoder equipped with Fourier Convolutional Blocks, allowing it to extract multiscale feature representations from an input ...
To obtain the downsampled disparity images {d(1s)}Ss=0, we have imple- mented a differentiable bilinear downsampling layer, sim- ilar to the sampler module in the spatial transformer net- works [13]. The first stage, DispFulNet, enlarges the half-resolution disparity estimates o...
Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized ...
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Swin transformer is used for image reconstruction. 3. Using UNet for high-level deep feature extraction. In this research, denoising is performed on the artificial noisy MRI pictures. Up and down sampling are used in the UNet module to retrieve deep features. Modern denoising models are ...
Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized ...
Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized ...
residual shrinkage layeradaptive transfer learningVibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of ...
Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized ...