Article
Optics
Mengyu Jia, Boshuai Sun, Yuxia Wang, Feng Gao, Zhiyong Yuan, Brian W. Pogue
Summary: Cherenkov imaging is a unique verification tool that provides both dosimetric and tissue functional information during radiation therapy. A noise-robust photon-limited imaging technique is proposed by exploiting the physical rationale of low-flux Cherenkov measurements and the spatial correlations of objects. Validation experiments confirm the promising recovery of Cherenkov signal with high signal-to-noise ratio and extended imaging depth in radiation oncology applications.
Article
Optics
Jing Han, Jinye Miao, Yingjie Shi, Shuo Zhu, Yan Sun, Lianfa Bai, Enlai Guo
Summary: This study proposes a highly sensitive approach to image through scattering media based on encoding speckles. It can reconstruct the target in a low illumination environment with high coding efficiency and noise resistance.
Article
Optics
Zhenghao Wang, Yongling Wu, Dongfeng Qi, Wenhui Yu, Hongyu Zheng
Summary: Metalenses consisting of nanoscale unit cells provide opportunities for miniaturization, light-weight, and diversification. Two-photon polymerization was used to directly fabricate single-layer metalenses, and their focusing performance was simulated and tested. The results demonstrate that TPP is a feasible and simplified approach for metalens fabrication.
OPTICS AND LASER TECHNOLOGY
(2024)
Article
Engineering, Mechanical
Liping Yu, Bing Pan
Summary: A novel time-gated active imaging DIC method based on bandpass filtering and gated single-photon imaging techniques is proposed for high-temperature deformation measurement. This method maintains image quality at high temperatures and shows promising application prospects.
EXTREME MECHANICS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Juan F. Florez-Ospina, Daniel L. Lau, Dominique Guillot, Kenneth Barner, Gonzalo R. Arce
Summary: This paper proposes a novel approach to compressive spectral imaging with panchromatic side information by utilizing approximate rank-order statistics. By assuming the smoothness of the signal of interest on an unknown graph and restricting it to the family of path graphs, the rank-order path graph induced by the signal is shown to be the best path. The approach utilizes the smoothness of rank-order path graphs inferred from rank-order statistics to obtain accurate spectral image estimates from compressed snapshots of the scene.
Article
Physics, Applied
Ting Luo, Lishun Wang, Xin Yuan
Summary: This paper reports on snapshot compressive spectral imaging using a grating and coded aperture. A vision-transformer-based deep learning algorithm is developed to achieve high-quality reconstruction. Experimental results demonstrate the reconstruction of over 190 spectral bands from a single measurement in the range of 485-657 nm, with a spectral accuracy of about 2 nm and a spectral resolution of up to 1.5 nm.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2023)
Article
Optics
Hong Zeng, Jiawei Dong, Qianxi Li, Weining Chen, Sen Dong, Huinan Guo, Hao Wang
Summary: This paper discusses the combination of single-pixel imaging and single photon-counting technology, and the use of compressed sensing algorithm for reconstruction. It proposes a novel sparse autoencoder network prior and the concept of multi-channel prior. The numerical gradient descent method is employed to solve the underdetermined linear equations. The experimental results show that the sparse autoencoder network prior can improve the reconstruction quality for single-photon counting compressed images.
Article
Instruments & Instrumentation
Taisei Ueki, Mizuki Uenomachi, Kenji Shimazoe, Hideki Tomita, Kei Kamada, Hiroyuki Takahashi
Summary: Nuclear medical imaging techniques such as SPECT and PET are important diagnostic methods in medicine, used to visualize radiolabeled molecules using gamma rays. This study investigated a novel method called double-photon emission imaging, which extracts information about the local microenvironment around a nucleus using the time-space correlation of emitted gamma rays. By applying a magnetic field, the researchers characterized the effect on a liquid-state radioisotope and discussed a method for estimating the location of the source using the strength of the magnetic field.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Md Saidur R. Pavel, Yimin D. Zhang
Summary: This paper proposes a neural network-based robust imaging method using compressive measurements from a two-dimensional array. The optimal compressive measurement is determined by maximizing the mutual information between the compressed measurement and the signal locations. A neural network-based strategy for localizing sources using these compressed measurements is then proposed. The proposed approach provides more robust performance compared to the conventional approach as it does not rely on any prior knowledge of received signals and the antenna configuration.
2022 56TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS
(2022)
Proceedings Paper
Optics
Cheng Jiang, Patrick Kilcullen, Yingming Lai, Siqi Wang, Tsuneyuki Ozaki, Jinyang Liang
Summary: This study develops multi-scale band-limited illumination profilometry (MS-BLIP) for robust 3D imaging of spatially isolated objects. MS-BLIP uses multi-frequency fringe projection with associated phase unwrapping and dual-level intensity projection to enhance dynamic range. It also applies a newly developed iterative method for distortion compensation and utilizes a dove prism to adjust the field of view orientation.
EMERGING DIGITAL MICROMIRROR DEVICE BASED SYSTEMS AND APPLICATIONS XV
(2023)
Article
Meteorology & Atmospheric Sciences
Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, Stephen Wright
Summary: This study presents a predictive model based on a graph-guided regularizer, which reduces the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. By using large ensemble simulations from a climate model to construct the regularizer, the structural uncertainty in the estimation is reduced.
JOURNAL OF CLIMATE
(2021)
Article
Statistics & Probability
Rungang Han, Rebecca Willett, Anru R. Zhang
Summary: This paper presents a flexible framework for generalized low-rank tensor estimation problems. By using the projected gradient descent method, the framework can adapt to the underlying low-rank structure in nonconvex problems, while providing statistical error bounds and linear convergence rates. The paper also considers a range of generalized tensor estimation problems and proves that the proposed algorithm achieves the minimax optimal convergence rate in estimation errors. Extensive experiments on simulated and real data demonstrate the superiority of the proposed framework.
ANNALS OF STATISTICS
(2022)
Article
Geochemistry & Geophysics
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett, Davis Gilton, Ian Foster
Summary: An automated rotation-invariant cloud clustering method utilizing deep learning technology is proposed to organize cloud imagery within large datasets without predefined classes. Evaluation results suggest that the resulting novel cloud clusters capture meaningful aspects of cloud physics, exhibit spatial coherence, and are invariant to image orientations.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett
Summary: As the impact of opaque predictive models on modern life continues to grow, there is a growing interest in quantifying the importance of input variables in making specific predictions. This paper proposes a fast and flexible method for approximating reduced models with important inferential guarantees. By linearizing and adding a ridge-like penalty, the method estimates the variable importance measure with a small error rate and provides confidence bounds for the estimates.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162
(2022)
Proceedings Paper
Engineering, Biomedical
Davis Gilton, Greg Ongie, Rebecca Willett
Summary: This study discusses the application of deep neural networks in biomedical imaging, proposing a novel retraining procedure to adapt to changes in the forward model, achieving robustness to changes in the forward model in image reconstruction.
2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Hyebin Song, Garvesh Raskutti, Rebecca Willett
Summary: The study introduces a new methodology and algorithm that can jointly estimate the magnitude and occurrence of events in the presence of missing labels. It is proven that the proposed method exhibits optimal statistical error even with a non-convex objective, and the gradient descent algorithm used has geometric convergence rates. Performance evaluation on synthetic data and a California wildfire dataset shows that the proposed method outperforms existing state-of-the-art approaches.
2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu
Summary: This paper presents a dynamic programming approach to estimate change points in high-dimensional linear regression models with improved performance and a computationally efficient refinement procedure to reduce localization error. Theoretical bounds on localization error and discussions on signal-to-noise conditions are also provided, supported by extensive numerical results and real air quality data experiments revealing historical change points.
24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS)
(2021)
Article
Engineering, Electrical & Electronic
Davis Gilton, Gregory Ongie, Rebecca Willett
Summary: Researchers have proposed two novel procedures that allow networks to adapt to changes in the forward model without full understanding of the change. These methods do not require access to more labeled data and have been successful in a variety of inverse problems such as deblurring, super-resolution, and undersampled image reconstruction in magnetic resonance imaging.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
Article
Mathematics, Applied
Greg Ongie, Daniel Pimentel-Alarcon, Laura Balzano, Rebecca Willett, Robert D. Nowak
Summary: This study extends low-rank matrix completion to cases where columns are points on a low-dimensional nonlinear algebraic variety, proposing a low algebraic dimension matrix completion algorithm that leverages existing LRMC methods on tensorized data representations. Mathematical justification is provided for the success of the method, including rank bounds of data in tensorized representation and sampling requirements for solution uniqueness. Experimental results show the new approach outperforming existing methods for matrix completion under a union-of-subspaces model.
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
(2021)
Article
Mathematics, Applied
Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
(2020)
Article
Engineering, Electrical & Electronic
Antonio G. Marques, Negar Kiyavash, Jose M. F. Moura, Dimitri van de Ville, Rebecca Willett
IEEE SIGNAL PROCESSING MAGAZINE
(2020)
Article
Meteorology & Atmospheric Sciences
Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, Rebecca M. Willett
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2020)
Article
Engineering, Electrical & Electronic
Davis Gilton, Greg Ongie, Rebecca Willett
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2020)
Article
Agriculture, Dairy & Animal Science
Brian D. Luck, Jessica L. Drewry, Randy D. Shaver, Rebecca M. Willett, Luiz F. Ferraretto
APPLIED ANIMAL SCIENCE
(2020)
Article
Engineering, Electrical & Electronic
Davis Gilton, Gregory Ongie, Rebecca Willett
Summary: This paper presents an alternative approach utilizing an infinite number of iterations, which consistently improves reconstruction accuracy beyond state-of-the-art alternatives; additionally, the computational budget can be chosen at test time to optimize context-dependent trade-offs between accuracy and computation.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
