Fall 2018

Computational Imaging Group (CIG) is proposing the following five projects for this Fall semester. If you are interested in a given project, please apply by emailing your resume to the project supervisor.

Learning for 3D computational imaging

Status: available
Description: Best computational imaging methods must exploit intricate relationships and features in the data. However, designing such methods for three-dimensional (3D) imaging is challenging due to the large amount of data. The goal of this project is to leverage the latest progress in machine learning to develop scalable algorithms capable of delivering highest quality results under high-data loads.
Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov

Convolutional dictionary learning for image reconstruction

Status: reserved (Fa Long)
Description: Convolutional dictionary learning (CDL) is a popular model for data-driven and unsupervised image reconstruction. However, the actual process of learning a dictionary is generally computationally intractable. In this project, the goal is to leverage recent advances in optimization and regularization theory to develop algorithms for efficient CDL. The key application in this project will be magnetic resonance imaging (MRI).
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov

Unsupervised deep image learning

Status: available
Description: One of the big challenges in applying deep learning to biomedical imaging is the lack of training data for supervised learning. The goal of this project is to overcome this limitation by looking into ways to reduce the dependence on the training data. The results of this project can be applied to large-scale imaging with limited datasets.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov

Nonconvex optimization for image restoration

Status: reserved (Jialong Zhang)
Description: Image restoration is often formulated as an optimization problem. Convex objective functions are often more desirable due to their computational tractability. However, there is a growing interest in going beyond convexity by considering nonconvex functions that better capture the geometry of the problem. The goal of this project is to develop analytical tools and methods for nonconvex image restoration.
Supervision: Ulugbek Kamilov (kamilov@wustl.edu)

Regularization with a convolutional neural network (CNN)

Status: reserved (Jiaming Liu)
Description: Convolutional neural networks (CNNs) have been successfully applied to numerous tasks in computational imaging and computer vision. Currently, their application for regularization of ill-posed inverse problems is a popular topic of research. In this project, the goal is to investigate various strategies of incorporating CNNs for stabilizing image reconstruction with applications to optical imaging.
Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov