arXiv:2609.13969v1 Announce Type: new
Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
By Wen-Chun Lin, Yu-Chee Tseng, Jen-Jee Chen, Nan-You Chen
The paper introduces a self‑supervised neural network that unifies single‑frame Fresnel coherent diffraction imaging (CDI) and overlapped ptychography. By using a fixed, pre‑estimated probe and optimizing with a Poisson negative log‑likelihood objective, the method reconstructs object patches from either a single diffraction frame or multiple overlapping measurements, achieving high SSIM scores and a ten‑fold improvement in photon‑dose efficiency. Demonstrations on synthetic patterns and real datasets from APS and LCLS show robust, high‑throughput reconstructions, with a 36× speedup over iterative solvers for a 10,304‑frame workload.
By Oliver Hoidn, Steven Henke, Albert Vong, Aashwin Mishra, Apurva Mehta, Matthew Seaberg
arXiv:2608. 02869v1 Announce Type: new Abstract: Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability.
By Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Apurva Mehta, David Shapiro, Alexander Hexemer, Nicholas Schwarz
arXiv:2512. 08444v2 Announce Type: replace-cross Abstract: Learned image reconstruction has become a pillar in computational imaging and inverse problems.
By Andreas Hauptmann, Ozan \"Oktem
arXiv:2608. 05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging.
By Alexander Auras, Martin Burger, Samira Kabri, Michael Moeller, Michael Schopf-Kuester
The paper presents a machine‑learning framework for reconstructing absorption and scattering coefficients in bilayered biological media from single‑distance, time‑resolved reflectance data. By training on a synthetic dataset generated with exact Monte Carlo simulations, the method outperforms traditional diffusion‑equation‑based inverse solvers in both speed and accuracy. It also estimates the dimensionality of the parameter space without prior knowledge of the number of layers, and suggests that future work could further improve accuracy using multi‑distance data.
By Caterina Amendola, Giulia Maffeis, Lorenzo Buffoni, Lorenzo Chicchi, Francesco Coghi, Duccio Fanelli, Raffaele Marino, Fabrizio Martelli, Riccardo Paoli, Lorenzo Pattelli, Lorenzo Spinelli