PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914071911071744 |
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| author | Gan, Weijie Zhai, Qiuchen McCann, Michael Thompson Cardona, Cristina Garcia Kamilov, Ulugbek S. Wohlberg, Brendt |
| author_facet | Gan, Weijie Zhai, Qiuchen McCann, Michael Thompson Cardona, Cristina Garcia Kamilov, Ulugbek S. Wohlberg, Brendt |
| contents | Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe. The image recovery from ptychographic data is generally achieved via an iterative algorithm that solves a nonlinear phase retrieval problem derived from measured diffraction patterns. However, these iterative approaches have high computational cost. In this paper, we introduce PtychoDV, a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction. PtychoDV comprises a vision transformer that generates an initial image from the set of raw measurements, taking into consideration their mutual correlations. This is followed by a deep unrolling network that refines the initial image using learnable convolutional priors and the ptychography measurement model. Experimental results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem, and significantly reduces computational cost compared to iterative methodologies, while maintaining competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07504 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction Gan, Weijie Zhai, Qiuchen McCann, Michael Thompson Cardona, Cristina Garcia Kamilov, Ulugbek S. Wohlberg, Brendt Image and Video Processing Computer Vision and Pattern Recognition Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe. The image recovery from ptychographic data is generally achieved via an iterative algorithm that solves a nonlinear phase retrieval problem derived from measured diffraction patterns. However, these iterative approaches have high computational cost. In this paper, we introduce PtychoDV, a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction. PtychoDV comprises a vision transformer that generates an initial image from the set of raw measurements, taking into consideration their mutual correlations. This is followed by a deep unrolling network that refines the initial image using learnable convolutional priors and the ptychography measurement model. Experimental results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem, and significantly reduces computational cost compared to iterative methodologies, while maintaining competitive performance. |
| title | PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.07504 |