Super Resolution Task

Super-resolution aims to reconstruct high-resolution images from lower-resolution inputs, a crucial task with applications ranging from medical imaging to satellite imagery analysis. Current research heavily utilizes deep learning, focusing on transformer-based architectures and their variations (e.g., incorporating wavelet transforms or large kernels) to improve efficiency and accuracy, alongside convolutional neural networks (CNNs) and diffusion models. These advancements are driving improvements in image quality and enabling applications requiring extremely high resolution, such as gigapixel-scale map generation and enhanced medical diagnostics.