Super Resolved

Super-resolution (SR) aims to enhance the resolution of images or other data, recovering fine details lost due to limitations in acquisition or processing. Current research heavily utilizes deep learning, particularly diffusion models, generative adversarial networks (GANs), and convolutional neural networks (CNNs), often incorporating attention mechanisms and wavelet transforms to improve efficiency and accuracy. These advancements are impacting diverse fields, from medical imaging (e.g., MRI) and remote sensing to microscopy and video processing, enabling more detailed analysis and improved diagnostic capabilities. The focus is increasingly on handling real-world complexities like noise, varying degradation types, and the need for efficient inference.