image reconstruction from incomplete data

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Deep Learning-Guided Image Reconstruction from Incomplete Data

An approach to incorporate deep learning within an iterative image reconstruction framework to reconstruct images from severely incomplete measurement data is presented. Specifically, we utilize a convolutional neural network (CNN) as a quasi-projection operator within a least squares minimization procedure. The CNN is trained to encode high level information about the class of images being ima...

Current Source Density Reconstruction from Incomplete Data

We propose two ways of estimating current source density (CSD) from measurements of voltage on a Cartesian grid with missing recording points using the inverse CSD method. The simplest approach is to substitute local averages (LA) in place of missing data. A more elaborate alternative is to estimate a smaller number of CSD parameters than the actual number of recordings and to take the least-sq...

Reconstruction of Smooth Functions from Incomplete Data

In many situations, one is confronted with the problem of trying to reconstruct an unknown continuous function from a finite set of data points or measurements where some of this data is incorrect, missing, or unreliable. If a contiguous set of data points is missing, the problem of reconstruction is highly ill-posed with an infinite dimensional solution set. However, if more information about ...

Image Reconstruction from Incomplete Data and Its Applications in Experimental Mechanics

In the field of experimental mechanics, there exist some circumstances when only data at the boundary can be obtained while the internal data are unavailable, or when some data are missed due to shadow, illumination saturation and other reasons. Thus it would be helpful if a reasonable estimation of the unavailable or missed data can be obtained. In this study, an algorithm is developed to reco...