Residual U Net

Residual U-Net architectures are a class of convolutional neural networks used primarily for image segmentation and denoising tasks, aiming to improve accuracy and efficiency compared to standard U-Nets. Current research focuses on enhancing these networks through modifications like incorporating Vision Transformers, employing deep supervision, and optimizing for specific applications such as medical image analysis (e.g., cardiac segmentation, stroke lesion detection, brain tumor segmentation, and nucleus segmentation) and remote sensing (e.g., land surface temperature super-resolution). These advancements are significantly impacting various fields by enabling more accurate and efficient analysis of medical images and other data types, leading to improved diagnostics and decision-making.