Computational Imaging Group (CIG) is looking for motivated WashU students for the following research projects for Spring 2020. If you are interested in a given project, please apply by emailing your resume to the project supervisor.

Learning for Video Reconstruction

Status: available Description: Video reconstruction is aimed to restore clear videos from corrupted observations, where state-of-the-art methods have achieved promising performance via supervised deep learning technologies. However, those methods require ground-truth videos, which may be inaccessible in some cases. Our goal is to develop a novel video reconstruction algorithm without the need of ground-truth. Supervision: Weijie Gan (weijie.gan@wustl.edu), Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Low-Level and Physics-Based Vision

Learning for Practical Video Compression

Status: reserved (Alan Zhu) Description: Video compression plays a critical role in internet traffic given limited bandwidth budget and increasing online content nowadays. Our goal is to develop novel and practical video compression algorithms especially when the required videos are in low-quality Supervision: Weijie Gan (weijie.gan@wustl.edu), Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Low-Level and Physics-Based Vision

• Machine learning for 4D MRI (W. Gan, H. An, U. S. Kamilov)
• Proximal Optimization for Computerized Tomography (R. Schurz, J. Liu, U. S. Kamilov)
• End-to-End Training of VAE-GAN Priors (G. Meng, J. Liu, U. S. Kamilov)
• Design of GAN Prios for Iterative Algorithms (A. Bani, J. Liu, U. S. Kamilov)
• Recovery Experiments for Deep Learning (J. Liu, Y. Sun, U. S. Kamilov)
• Model-based and Learning-based Computerized Tomography (M. Xie, Y. Sun, U. S. Kamilov)
• Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
• Fast Automatic Focal Plane Selection for Brightfield Microscopy using Machine Learning (Y. Song, X. Xu, N. Laird, Z. Pincus, U. S. Kamilov)
• Nonlocal Learning for Image Restoration (Y. Li, X. Xu, U. S. Kamilov)
• Motion-artifact Correction in Quantitative MRI (H. Tang, X. Xu, U. S. Kamilov)

• Motion artifact reduction in quantitative brain MRI (N. Woythaler, X. Xu, U. S. Kamilov)
• Large-Scale Optimization for Computational Imaging (Y. Jia, U. S. Kamilov)
• Generative Adversarial Networks for Image Restoration (R. Suzuki, X. Xu, U. S. Kamilov)
• Adversarially robust classifiers for image reconstruction (E. Tang, X. Xu, U. S. Kamilov)
• Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
• Asynchronous large-scale optimization for imaging (Y. Sun, Y. Sun, U. S. Kamilov)
• Next generation Cryo-EM imaging (M. Xie, Y. Sun, U. S. Kamilov)
• Computational methods for quantitative brain MR imaging (M. Torop, Y. Sun, U. S. Kamilov)

• Deep Single-Molecule Super-Resolution Imaging (S. Xu, Y. Sun, H. Mazidi, M. Lew, U. S. Kamilov)
• Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
• Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)

• Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)
• Learning for 3D computational imaging (W. Gan, X. Xu, U. S. Kamilov)
• Convolutional dictionary learning for image reconstruction (F. Long, X. Xu, U. S. Kamilov)
• Unsupervised deep image learning (H. Zou, Y. Sun, U. S. Kamilov)
• Distributed algorithms for nonconvex stochastic optimization (M. Abijaoude, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Image restoration with GANs (F. Zhai, Y. Sun, U. S. Kamilov)
• Learning for MRI (J. Xing, X. Xu, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)