Image SR

Image super-resolution (SR) aims to enhance the resolution of low-resolution images, producing higher-quality images with finer details. Current research focuses on improving the perceptual quality of SR images, exploring techniques like diffusion models and attention mechanisms to address issues such as blurriness and inconsistencies, particularly in novel view synthesis from limited input. This field is driven by the need for high-resolution images in various applications, from e-commerce and virtual reality to medical imaging, and ongoing advancements are constantly pushing the boundaries of achievable performance and efficiency. The development of self-supervised learning methods and efficient architectures for large images are also significant areas of active investigation.