Sparse View Image

Sparse view image processing focuses on reconstructing high-quality images or 3D models from a limited number of input views, addressing the challenge of underconstrained data. Current research emphasizes developing efficient and accurate neural rendering methods, often employing transformer-based architectures or U-Net variations, to improve image quality and 3D reconstruction from sparse data, sometimes incorporating diffusion models for enhanced performance. This field is crucial for applications ranging from medical imaging (e.g., reducing radiation exposure in CT scans) to augmented and virtual reality (e.g., real-time light field generation), where acquiring dense view data is impractical or impossible. The development of robust and efficient algorithms is driving progress in these diverse areas.