Super Resolution Microscopy

Super-resolution microscopy aims to overcome the diffraction limit of light, enabling visualization of biological structures at the nanoscale. Current research heavily utilizes deep learning, employing architectures like transformers and generative models (e.g., diffusion models and GANs) to enhance image resolution from lower-resolution inputs, often leveraging self-supervised or weakly-supervised learning techniques to address data scarcity. These advancements are significantly impacting biological research by improving the quality and speed of nanoscale imaging, facilitating more precise analysis of dynamic cellular processes and potentially enabling new discoveries in fields like diagnostics and materials science.