| Abstract: |
| Ptychography has emerged as a premier lensless imaging technique, offering sub-Angstrom resolution and exceptional phase contrast for characterizing nanomaterials. However, traditional 2D models rely on the thin-slice approximation, which fails to account for multiple scattering in thick or strongly scattering specimens. Existing multi-slice ptychography (msPtycho) algorithms, such as 3PIE, are often derived heuristically, lack rigorous convergence guarantees, and suffer from slow convergence or heavy computational burdens. To address these challenges, this talk introduces a novel chain-structured modeling approach that reduces complex multi-linear couplings into a sequence of bilinear relations. Based on this framework, we present the AM2SP algorithm, providing closed-form solutions and proven global convergence to stationary points. By incorporating forward-propagating correction and Nesterov-like extrapolation, the enhanced AM2SP-FX algorithm significantly suppresses error accumulation and accelerates reconstruction speed by over an order of magnitude compared to traditional baselines. Furthermore, we explore the integration of Implicit Neural Representation (INR) to maintain high-fidelity reconstruction quality even under large-step scanning geometries with low overlap ratios. This synergy between rigorous physical modeling and continuous neural representation offers a robust, scalable solution for high-resolution 3D imaging. |
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