Reconstruction Algorithm

Reconstruction algorithms aim to recover high-quality images or 3D models from incomplete or noisy data, a crucial task across diverse fields like medical imaging, robotics, and astronomy. Current research emphasizes leveraging deep learning architectures, such as convolutional neural networks, transformers, and recurrent neural networks, often integrated with model-based approaches to improve accuracy, speed, and robustness. These advancements are significantly impacting various applications by enabling faster data acquisition, higher resolution imaging, and more accurate scientific inferences from limited measurements, particularly in scenarios with low signal-to-noise ratios or missing data. Furthermore, research is actively exploring the use of hybrid methods combining deep learning with traditional algorithms and the development of efficient GPU-accelerated implementations.