Image Super Resolution

Image super-resolution (SR) aims to enhance the resolution of low-resolution images, improving their visual quality and detail. Current research heavily focuses on leveraging deep learning models, particularly diffusion models and transformers, often incorporating techniques like attention mechanisms and multi-scale feature extraction to achieve efficient and high-quality results. These advancements are driving improvements in various applications, including broadcast video enhancement, remote sensing image analysis, and medical imaging, where high-resolution images are crucial for accurate interpretation and analysis. Furthermore, research is actively exploring methods to improve the efficiency and robustness of SR models, particularly for deployment on resource-constrained devices.