Upsampling Module

Upsampling modules are crucial components in many deep learning architectures, aiming to increase the resolution of feature maps or signals, a critical step in tasks like image segmentation, super-resolution, and video processing. Current research focuses on improving upsampling methods' efficiency and accuracy, exploring techniques like learnable interpolation, attention mechanisms, and novel architectures such as U-Net variants and Transformers, to mitigate artifacts and improve performance. These advancements have significant implications for various applications, enhancing the quality and efficiency of image and video processing, medical imaging analysis, and other fields relying on high-resolution data.