Iterative Inversion

Iterative inversion techniques aim to efficiently solve inverse problems, where the goal is to recover an unknown input from observed outputs. Current research focuses on improving the speed and accuracy of these methods, employing diverse approaches such as neural networks (e.g., incorporating physics-based constraints or multi-frequency data), refined noise approximation strategies, and bidirectional consistency models for faster convergence. These advancements are impacting various fields, including medical imaging, geophysical prospecting, and generative image modeling, by enabling faster and more accurate reconstruction of images and signals from incomplete or noisy data.