Diffusion Based Super Resolution

Diffusion-based super-resolution (SR) aims to generate high-resolution images from low-resolution inputs by leveraging the power of diffusion models, primarily focusing on improving efficiency and image quality. Current research emphasizes developing novel architectures and algorithms to reduce the number of computationally expensive sampling steps required, often incorporating techniques like knowledge distillation, latent space processing, and patch-based approaches. This field is significant because it offers a powerful alternative to traditional SR methods, potentially impacting various applications such as medical imaging, digital pathology, and general image enhancement by producing higher-quality results with faster inference times.