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.

Motion-artifact correction in quantitative MRI

Status: available Description: Noninvasive imaging is the holy grail of medical brain diagnostics. One popular technology used for this purpose is quantitative magnetic resonance imaging (MRI). However, quantitative MRI typically requires long acquisition times and suffers from motion artifacts due to patient motion during scanning. Our goal is to build on the recent algorithms developed in CIG to reduce motion artifacts in quantitative MR images using deep learning. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Learning for Imaging

Machine learning to see through scattering materials

Status: reserved (Alan Zhu) Description: Thick biological tissues give rise to the multiple scattering of light waves which complicates noninvasive microscopic imaging. The challenge for existing optical microscopes to overcome scattering has limited the high-resolution imaging to shallow depths. In this project, the goal is to use machine learning to overcome the fundamental scattering limit in tissue imaging. CIG has several contributions to the area, but would like to push the technology beyond its current limit. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Learning for Imaging, Computational Microscopy

Computational imaging using the deep image prior

Status: reserved (Walter Wang) Description: Deep image prior (DIP) is an elegant strategy that relies on an untrained convolutional neural network to restore an image. CIG has developed a powerful extension to DIP, called DIP-TV, which significantly improves over the original formulation by offering better imaging quality and more stable performance. This project aims to further improve DIP-TV to enable practical imaging. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Image Restoration, Learning for Imaging

Nonlocal learning for image restoration

Status: reserved (Yukun Li) Description: Deep learning has become the method of choice for image restoration. However, prior to the deep-learning revolution, non-local image restoration methods, such as BM3D, were the dominant algorithms in the field. In this project, the goal is to reconcile deep learning with non-local algorithms and come up with a new generation of powerful image restoration methods. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov Areas: Image Restoration, Learning for Imaging

Recovery guarantees for deep learning

Status: reserved (Peter Ming) Description: Traditional sampling theory comes with elegant strategies for both data acquisition and signal reconstruction. Sampling is performed at twice the Nyquist rate, while the reconstruction is achieved with simple low-pass filtering. However, deep learning algorithms do not come with such elegant theoretical background, which we intend to fix in this project. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Compressive Imaging, Learning for Imaging

Generative adversarial priors for image reconstruction

Status: available Description: Generative adversarial networks (GANs) provide a powerful mechanism to represent visually important image features. However, GANs require pretty careful handling during training. In this project, our goal is to develop GAN-based algorithms for image reconstruction. In particular, our goal is to combine GANs with physics-based information for improved performance. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Compressive Imaging, Learning for Imaging

Adaptive algorithms for computational imaging

Status: available Description: Computational imaging relies on advanced algorithms for forming high-quality images. However, such algorithms are typically fixed and have limited capability to adapt to new data. In this project, our goal is to develop the next generation of algorithms that can adapt “on the go” to the changes in the imaging performance. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov Areas: Compressive Imaging, Learning for Imaging

Learning for magnetic resonance imaging (MRI)

Status: active (Weijie Gan) Description: Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. One of the key practical limitations of MRI is the long duration of data acquisition. The goal of this project is to reduce the scan times by using deep-learning-based imaging techniques. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Learning for Imaging

Fast Automatic Focal Plane Selection for Brightfield Microscopy using Machine Learning

Status: available Description: The lack of accurate autofocus makes it difficult to analyze the data in brightfield microscopy. The goal of this collaborative project is to develop a method that would compute the amount/direction the microscope should move to ensure an in-focus image. We have available z-stacks of brightfield lab-frame images with labeled in-focus images. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Nicolette Laird, Zachary Pincus, Ulugbek Kamilov Areas: Computational Microscopy, Image Analysis

Automatic Biological Event Detection in Brightfield Microscopy using Machine Learning

Status: available Description: This is a collaborative project with the goal of analyzing brightfield microscopy data with c. elegans worms. In this project, the student will develop a classifier that will detect if the worm is dead or living. This is achieved by analyzing a certain number of frames from the microscope and using machine learning. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Nicolette Laird, Zachary Pincus, Ulugbek Kamilov Areas: Computational Microscopy, Image Analysis

• 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 (R. 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 (R. 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)