Brain parcellation is important for exploiting neuroimaging data. Variability in physiology between individuals has led to the need for data-driven approaches to parcellation, with recent research focusing on simultaneously estimating and partitioning the network structure of the brain. We view
A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI
Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples. We
Fast and robust estimation of ophthalmic wavefront aberrations
Rapidly rising levels of myopia, particularly in the developing world, have led to an increased need for inexpensive and automated approaches to optometry. A simple and robust technique is provided for estimating major ophthalmic aberrations using a gradient-based wavefront sensor.
Computational estimation of resolution in reconstruction techniques utilizing sparsity, total variation, and nonnegativity
Techniques which exploit properties such as sparsity and total variation have provided the ability to reconstruct images that surpass the conventional limits of imaging. This leads to difficulties in assessing the result, as conventional metrics for resolution are no longer
Imposing uniqueness to achieve sparsity
In this paper we take a novel approach to the regularization of underdetermined linear systems. Typically, a prior distribution is imposed on the unknown to hopefully force a sparse solution, which often relies on uniqueness of the regularized solution (something
Element-wise uniqueness, prior knowledge, and data-dependent resolution
Techniques for finding regularized solutions to underdetermined linear systems can be viewed as imposing prior knowledge on the unknown vector. The success of modern techniques, which can impose priors such as sparsity and non-negativity, is the result of advances in
Bounding pixels in computational imaging
We consider computational imaging problems where we have an insufficient number of measurements to uniquely reconstruct the object, resulting in an ill-posed inverse problem. Rather than deal with this via the usual regularization approach, which presumes additional information which may
Depth sectioning of attenuation
We derive an approach for imaging attenuative sample parameters with a confocal scanning system. The technique employs computational processing to form the estimate in a pixel-by-pixel manner from measurements at the Fourier plane, rather than detecting a focused point at
Computational confocal tomography for simultaneous reconstruction of objects, occlusions, and aberrations
We introduce and experimentally validate a computational imaging technique that employs confocal scanning and coherent detection in the Fourier domain. We show how this method may be used to tomographically reconstruct attenuation, aberration, and even occlusion. We also show how
Bilinear wavefront transformation
Truncated expansions such as Zernike polynomials provide a powerful approach for describing wavefront data. However, many simple calculations with data in this form can require significant computational effort. Important examples include recentering, renormalizing, and translating the wavefront data. This paper