# Using a modified double deep image prior for crosstalk mitigation in multislice ptychography

arXiv: https://arxiv.org/abs/2102.00869 · HTML: https://mlsanity.com/paper/2102.00869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2021.

## Built on by (most cited first, 3 of 3)

- [A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging](https://mlsanity.com/paper/2412.06806/) (2024)
- [Fidelity-preserving enhancement of ptychography with foundational text-to-image models](https://mlsanity.com/paper/2509.04513/) (2025)
- [Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learning](https://mlsanity.com/paper/2609.25684/) (2026)

## Abstract (arXiv)

Multislice ptychography is a high-resolution microscopy technique used to image multiple separate axial planes using a single illumination direction. However, multislice ptychography reconstructions are often degraded by crosstalk, where some features on one plane erroneously contribute to the reconstructed image of another plane. Here, we demonstrate the use of a modified "double deep image prior" (DDIP) architecture in mitigating crosstalk artifacts in multislice ptychography. Utilizing the tendency of generative neural networks to produce natural images, a modified DDIP method yielded good results on experimental data. For one of the datasets, we show that using DDIP could remove the need of using additional experimental data, such as from x-ray fluorescence, to suppress the crosstalk. Our method may help x-ray multislice ptychography work for more general experimental scenarios.