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PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval
Image and Video Processing · Submitted 22 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We also propose the extended-PtychoFormer (ePF), a hybrid approach that combines the benefits of PtychoFormer with the ePIE. ePF minimizes global phase shifts and significantly enhances reconstruction quality, achieving state-of-the-art phase retrieval in ptychography.
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The abstract, sentence by sentence
Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. While conventional phase retrieval algorithms can iteratively recover the images, they require oversampled diffraction patterns, incur significant computational costs, and struggle to recover the absolute phase of the sample's transmission function. Deep learning algorithms for ptychography are a promising approach to resolving the limitations of iterative algorithms. We present PtychoFormer, a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval. PtychoFormer processes subsets of diffraction patterns, generating local inferences that are seamlessly stitched together to produce a high-quality reconstruction. Our model exhibits tolerance to sparsely scanned diffraction patterns and achieves up to 3600 times faster imaging speed than the extended ptychographic iterative engine (ePIE). We also propose the extended-PtychoFormer (ePF), a hybrid approach that combines the benefits of PtychoFormer with the ePIE. ePF minimizes global phase shifts and significantly enhances reconstruction quality, achieving state-of-the-art phase retrieval in ptychography.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | While conventional phase retrieval algorithms can iteratively recover the images, they require oversampled diffraction patterns, incur significant computational costs, and struggle to recover the absolute phase of the sample's transmission function. | background 0.60 | 0.05 | 0.03 | asserted 1.00 |
| s2 | Deep learning algorithms for ptychography are a promising approach to resolving the limitations of iterative algorithms. | background 0.98 | 0.05 | 0.04 | hedged possibility 0.51 |
| s3 | We present PtychoFormer, a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval. | contribution 0.98 | 0.92 | 0.16 | asserted 1.00 |
| s4 | PtychoFormer processes subsets of diffraction patterns, generating local inferences that are seamlessly stitched together to produce a high-quality reconstruction. | method 1.00 | 0.81 | 0.09 | asserted 1.00 |
| s5 | Our model exhibits tolerance to sparsely scanned diffraction patterns and achieves up to 3600 times faster imaging speed than the extended ptychographic iterative engine (ePIE). | result 1.00 | 0.95 | 0.26 | asserted 1.00 |
| s6 | We also propose the extended-PtychoFormer (ePF), a hybrid approach that combines the benefits of PtychoFormer with the ePIE. ePF minimizes global phase shifts and significantly enhances reconstruction quality, achieving state-of-the-art phase retrieval in ptychography. | contribution 0.91 | 0.93 | 0.54 | asserted 1.00 |
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Original abstract
Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. While conventional phase retrieval algorithms can iteratively recover the images, they require oversampled diffraction patterns, incur significant computational costs, and struggle to recover the absolute phase of the sample's transmission function. Deep learning algorithms for ptychography are a promising approach to resolving the limitations of iterative algorithms. We present PtychoFormer, a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval. PtychoFormer processes subsets of diffraction patterns, generating local inferences that are seamlessly stitched together to produce a high-quality reconstruction. Our model exhibits tolerance to sparsely scanned diffraction patterns and achieves up to 3600 times faster imaging speed than the extended ptychographic iterative engine (ePIE). We also propose the extended-PtychoFormer (ePF), a hybrid approach that combines the benefits of PtychoFormer with the ePIE. ePF minimizes global phase shifts and significantly enhances reconstruction quality, achieving state-of-the-art phase retrieval in ptychography.
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DL methods also exhibit more tolerance to sparse scan patterns, reducing the need to capture numerous diffraction patterns for the region of interest [ref] .
Real-time sparse-sampled Ptychographic imaging through deep neural networks · Introduction · 0.97
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