Resolution Reconstruction

Resolution reconstruction aims to enhance the detail and fidelity of images and 3D models from limited or degraded input data. Current research focuses on deep learning approaches, employing architectures like neural networks (including convolutional and transformer-based models) and implicit functions, often incorporating iterative refinement and self-supervised learning techniques to improve generalization and handle complex degradations like noise and motion blur. These advancements are significantly impacting various fields, including medical imaging (MRI, CT), computer vision (image super-resolution, 3D reconstruction), and experimental fluid mechanics, by enabling higher-quality reconstructions from limited or noisy data, leading to improved diagnostic capabilities and scientific understanding.