Inverse Scattering

Inverse scattering aims to reconstruct the properties of an object from measurements of scattered waves, a challenging problem due to its ill-posed nature and computational complexity. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks and diffusion models within iterative frameworks (e.g., Born iterative methods) or as generative models incorporating physical priors to improve reconstruction accuracy and stability. These advancements offer significant potential for enhancing imaging techniques across diverse fields, including medical imaging, non-destructive testing, and remote sensing, by providing faster and more robust solutions than traditional methods.