Medical Image Restoration

Medical image restoration aims to improve the quality of medical images by removing noise, enhancing resolution, or recovering missing information, ultimately improving diagnostic accuracy and treatment planning. Recent research heavily utilizes transformer-based architectures, exploring novel attention mechanisms to efficiently process high-resolution images and address challenges like irrelevant region interference. Furthermore, research focuses on developing all-in-one models capable of handling multiple restoration tasks simultaneously and on accelerating inference speeds through alternative approaches like implicit Schrödinger bridges. These advancements hold significant promise for improving the efficiency and effectiveness of medical image analysis across various modalities.