2021arXiv (Cornell University)Open access

Improving Acquisition Speed of X-Ray Ptychography through Spatial\n Undersampling and Regularization

Prasan Shedligeri, Florian Schiffers, Semih Barutcu, Pablo D. Ruiz, Aggelos K. Katsaggelos, Oliver Cossairt

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Abstract

X-ray ptychography is one of the versatile techniques for nanometer\nresolution imaging. The magnitude of the diffraction patterns is recorded on a\ndetector and the phase of the diffraction patterns is estimated using phase\nretrieval techniques. Most phase retrieval algorithms make the solution\nwell-posed by relying on the constraints imposed by the overlapping region\nbetween neighboring diffraction pattern samples. As the overlap between\nneighboring diffraction patterns reduces, the problem becomes ill-posed and the\nobject cannot be recovered. To avoid the ill-posedness, we investigate the\neffect of regularizing the phase retrieval algorithm with image priors for\nvarious overlap ratios between the neighboring diffraction patterns. We show\nthat the object can be faithfully reconstructed at low overlap ratios by\nregularizing the phase retrieval algorithm with image priors such as\nTotal-Variation and Structure Tensor Prior. We also show the effectiveness of\nour proposed algorithm on real data acquired from an IC chip with a coherent\nX-ray beam.\n

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X-ray ptychography is one of the versatile techniques for nanometer\nresolution imaging. The magnitude of the diffraction patterns is recorded on a\ndetector and the phase of the diffraction patterns is estimated using phase\nretrieval techniques. Most phase retrieval algorithms make the solution\nwell-posed by relying on the constraints imposed by the overlapping region\nbetween neighboring diffraction pattern samples. As the overlap between\nneighboring diffraction patterns reduces, the problem becomes ill-posed and the\nobject cannot be recovered. To avoid the ill-posedness, we investigate the\neffect of regularizing the phase retrieval algorithm with image priors for\nvarious overlap ratios between the neighboring diffraction patterns. We show\nthat the object can be faithfully reconstructed at low overlap ratios by\nregularizing the phase retrieval algorithm with image priors such as\nTotal-Variation and Structure Tensor Prior. We also show the effectiveness of\nour proposed algorithm on real data acquired from an IC chip with a coherent\nX-ray beam.\n

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Available abstract

X-ray ptychography is one of the versatile techniques for nanometer\nresolution imaging. The magnitude of the diffraction patterns is recorded on a\ndetector and the phase of the diffraction patterns is estimated using phase\nretrieval techniques. Most phase retrieval algorithms make the solution\nwell-posed by relying on the constraints imposed by the overlapping region\nbetween neighboring diffraction pattern samples. As the overlap between\nneighboring diffraction patterns reduces, the problem becomes ill-posed and the\nobject cannot be recovered. To avoid the ill-posedness, we investigate the\neffect of regularizing the phase retrieval algorithm with image priors for\nvarious overlap ratios between the neighboring diffraction patterns. We show\nthat the object can be faithfully reconstructed at low overlap ratios by\nregularizing the phase retrieval algorithm with image priors such as\nTotal-Variation and Structure Tensor Prior. We also show the effectiveness of\nour proposed algorithm on real data acquired from an IC chip with a coherent\nX-ray beam.\n

Key concepts: Phase retrieval, Ptychography, Undersampling, Diffraction, Coherent diffraction imaging, Algorithm, Prior probability, Regularization (linguistics)

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