AirNet-SNL: End-To-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT
Abstract not provided.
Publications
Mask design and fabrication for higher energy speckle-based XPCI
Abstract not provided.
AirNet-SNL: End-to-end training of iterative reconstruction and deep neural network regularization for sparse-data XPCI CT
Optics InfoBase Conference Papers
We present a deep learning image reconstruction method called AirNet-SNL for sparse view computed tomography. It combines iterative reconstruction and convolutional neural networks with end-to-end training. Our model reduces streak artifacts from filtered back-projection with limited data, and it trains on randomly generated shapes. This work shows promise to generalize learning image reconstruction.
Phase Retrieval using Consensus Equilibrium in X-Ray Phase Contrast Imaging
Abstract not provided.
Distinguishing one from many using super-resolution compressive sensing
Abstract not provided.
Finite Set Statistics Based Multitarget Tracking
Abstract not provided.
Purdue LDRD Closeout
Abstract not provided.
Properties of Ambiguity Functions
Abstract not provided.
Creating a Telescopic Data Acquisition and Analysis System from COTS Products
Abstract not provided.
9 Results