Speaker
Description
Ptychography is a powerful coherent diffraction imaging technique essential for reconstructing high-resolution, complex-valued images from intensity-only measurements. However, the reconstruction poses significant challenges due to its nonconvex and ill-posed nature. We propose a novel multilevel optimization framework emphasizing stochastic learning principles to efficiently address these challenges. By formulating the inverse problem as iterative minimization of quadratic surrogate models at varying resolution levels, our method strategically utilizes stochastic gradient evaluations, significantly reducing computational overhead. The multilevel structure exploits hierarchical, multi-scale information intrinsic to the imaging data, enabling faster and more stable convergence compared to traditional deterministic approaches. Hyperparameters are automatically adjusted across resolution levels, ensuring robustness and scalability. Numerical results demonstrate that our stochastic multilevel optimization substantially enhances both reconstruction accuracy and computational efficiency, presenting a robust solution for large-scale ptychographic imaging applications.