TY - GEN
T1 - IMPROVING ACQUISITION SPEED OF X-RAY PTYCHOGRAPHY THROUGH SPATIAL UNDERSAMPLING AND REGULARIZATION
AU - Shedligeri, Prasan
AU - Schiffers, Florian
AU - Barutcu, Semih
AU - Ruiz, Pablo
AU - Katsaggelos, Aggelos K.
AU - Cossairt, Oliver
N1 - Funding Information:
This work was supported in part by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). The work was also funded in part by NSF CAREER IIS-1453192. The research is based in part upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA-16003-165043 contract number. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Prasan Shedligeri was supported by a Research Travel Scholarship from Robert Bosch Center for Data Science and Artificial Intelligence (RBC-DSAI), IIT Madras, India.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - X-ray ptychography is one of the versatile techniques for nanometer resolution imaging. The magnitude of the diffraction patterns is recorded on a detector, and the phase of the diffraction patterns is estimated using phase retrieval techniques. Most phase retrieval algorithms make the solution well-posed by relying on the constraints imposed by the overlapping region between neighboring diffraction pattern samples. As the overlap between neighboring diffraction patterns reduces, the problem becomes ill-posed, and the object cannot be recovered. To avoid the ill-posedness, we investigate the effect of regularizing the phase retrieval algorithm with image priors for various overlap ratios between the neighboring diffraction patterns. We show that the object can be faithfully reconstructed at low overlap ratios by regularizing the phase retrieval algorithm with image priors such as Total-Variation prior and Structure Tensor Prior. We also show the effectiveness of our proposed algorithm on real data acquired from an IC chip with a coherent X-ray beam.
AB - X-ray ptychography is one of the versatile techniques for nanometer resolution imaging. The magnitude of the diffraction patterns is recorded on a detector, and the phase of the diffraction patterns is estimated using phase retrieval techniques. Most phase retrieval algorithms make the solution well-posed by relying on the constraints imposed by the overlapping region between neighboring diffraction pattern samples. As the overlap between neighboring diffraction patterns reduces, the problem becomes ill-posed, and the object cannot be recovered. To avoid the ill-posedness, we investigate the effect of regularizing the phase retrieval algorithm with image priors for various overlap ratios between the neighboring diffraction patterns. We show that the object can be faithfully reconstructed at low overlap ratios by regularizing the phase retrieval algorithm with image priors such as Total-Variation prior and Structure Tensor Prior. We also show the effectiveness of our proposed algorithm on real data acquired from an IC chip with a coherent X-ray beam.
KW - Automatic differentiation
KW - Phase-retrieval
KW - Ptychography
KW - Regularization
UR - http://www.scopus.com/inward/record.url?scp=85125601761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125601761&partnerID=8YFLogxK
U2 - 10.1109/ICIP42928.2021.9506086
DO - 10.1109/ICIP42928.2021.9506086
M3 - Conference contribution
AN - SCOPUS:85125601761
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2968
EP - 2972
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
T2 - 2021 IEEE International Conference on Image Processing, ICIP 2021
Y2 - 19 September 2021 through 22 September 2021
ER -