In recent years, deep learning has achieved remarkable empirical success for image
reconstruction. This has catalyzed an ongoing quest for the precise characterization of the …

Light-sheet fluorescence microscopy has transformed our ability to visualize and
quantitatively measure biological processes rapidly and over long time periods. In this …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

In this paper we describe an investigation into the application of deep learning methods for
low-dose and sparse angle computed tomography using small training datasets. To motivate …

Geographic spaces is a vast and complex system involving multiple elements and nonlinear
interactions of these elements, and rich in geographical phenomena, processes and …

Recovering a high-quality image from noisy indirect measurements is an important problem
with many applications. For such inverse problems, supervised deep convolutional neural …

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for
reconstructing the internal structure of materials at high spatial resolutions from tens of …

A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty
Source's Perspective Page 1 A Survey on Uncertainty Quantification Methods for Deep Neural …

Image denoising is a well-known and well studied problem, commonly targeting a
minimization of the mean squared error (MSE) between the outcome and the original image …

Inverse problems are ubiquitous in nature, arising in almost all areas of science and
engineering ranging from geophysics and climate science to astrophysics and …