Electromagnetic signal denoising model based on stacked ET layers structure
Y Huang, J Mao, K Huang, J Liu, N Tang - Measurement, 2025 - Elsevier
Electromagnetic signal denoising is crucial for safeguarding sensitive data from
electromagnetic leakage. We introduce the Adaptive Stacked Temporal Pyramid Network …
electromagnetic leakage. We introduce the Adaptive Stacked Temporal Pyramid Network …
[HTML][HTML] Multi-Scale Detail–Noise Complementary Learning for Image Denoising
Y Cui, M Shi, J Jiang - Applied Sciences, 2024 - mdpi.com
Deep convolutional neural networks (CNNs) have demonstrated significant potential in
enhancing image denoising performance. However, most denoising methods fuse different …
enhancing image denoising performance. However, most denoising methods fuse different …
GUFORMER: a gradient-aware U-shaped transformer neural network for real image denoising
X Bai, Y Wan, W Wang, B Zhou - The Journal of Supercomputing, 2025 - Springer
Real-world image denoising is important in image analysis such as image enhancement,
object tracking, and recognition. However, the traditional deep learning-based methods …
object tracking, and recognition. However, the traditional deep learning-based methods …
Dynamic Convolution Neural Networks with Both Global and Local Attention for Image Classification
C Zheng, Y Li, J Li, N Li, P Fan, J Sun, P Liu - Mathematics, 2024 - mdpi.com
Convolution is a crucial component of convolution neural networks (CNNs). However, the
standard static convolution has two primary defects: data independence and the weak ability …
standard static convolution has two primary defects: data independence and the weak ability …