Medical Image Super Resolution

Medical image super-resolution (MISR) aims to enhance the resolution of low-resolution medical images, improving diagnostic accuracy and efficiency. Current research focuses on developing advanced deep learning models, including convolutional neural networks (CNNs), transformers, and implicit neural representations (INRs), often incorporating innovative techniques like attention mechanisms and closed-loop feedback for improved performance. These advancements are driven by the need for higher-quality images in various medical imaging modalities, ultimately impacting clinical workflows and potentially leading to better diagnostic outcomes. The field is also exploring methods to address challenges like domain adaptation and efficient processing of large datasets.