Author: Keith

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

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

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

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

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