Between 2011 and 2017, errors rates on the ImageNet Large-Scale Visual Recognition Challenge dropped from 25.8% to 2.25%; this improvement was driven by the development of convolutional neural networks (CNNs). Now, a plethora of CNN-based approaches are being applied to inverse problems in imaging. Should we expect the same dramatic improvements here? In this talk, I will survey some of the progress so far, including our recent work on X-ray computed tomography reconstruction.
Measure Digital, Reconstruct Analog16 Apr 2019
Inner-Loop-Free ADMM for Cryo-EM15 Jan 2019
Fast PET reconstruction: the home stretch11 Dec 2018
Self-Supervised Deep Active Accelerated MRI27 Nov 2018
Minimum Support Multi-Splines20 Nov 2018