Computational Reconstruction

Computational reconstruction focuses on algorithmically recovering high-quality images or signals from incomplete or noisy data, aiming to improve efficiency and accuracy compared to traditional methods. Current research emphasizes the development and application of deep learning models, including convolutional neural networks and generative adversarial networks, often combined with iterative optimization techniques and incorporating prior information (e.g., from a similar image) to enhance reconstruction quality. These advancements are significantly impacting various fields, such as medical imaging (MRI, CT), compressed sensing, and federated learning, by enabling faster, more accurate, and robust image formation from limited data.