Single Image Super Resolution

Single image super-resolution (SISR) aims to enhance the resolution of low-resolution images using only the information contained within a single image. Current research focuses on improving the efficiency and accuracy of SISR, exploring architectures like transformers, convolutional neural networks, and diffusion models, often incorporating techniques such as attention mechanisms and multi-scale processing to better capture and reconstruct high-frequency details. These advancements are significant for various applications, including medical imaging, remote sensing, and document processing, where high-resolution images are crucial but acquiring them directly may be impractical or expensive. The field is also actively addressing challenges like real-world degradations and resource constraints, leading to the development of more robust and efficient algorithms.