The proposed feature extractor, nonnegative linear reconstruction projection (NLRP), and NLRC share a common decisional rule, and thus the features extracted by NLRP fit NLRC well in theory.
We present a novel software package for the problem "reconstruction from projections" in electron microscopy.
Problems of image reconstruction from projections can be represented by a system of linear equations A x = b.
While applicable in other inverse problem domains (e.g. astronomy, geophysics, signal processing or remote sensing), this class of algorithms was designed for tomographic image reconstruction from projections in medicine.
To construct a demographic database consistent with our economic model (i.e., a unisex closed population), we combine two demographic methods widely used in population reconstruction: Inverse Projection (IP) and Generalized Inverse Projection (GIP) (Lee 1985; Oeppen 1993).
To produce a high-quality reconstruction, the projection images must be aligned accurately.
Reconstruction of projection data in single photon emission computed tomography (SPECT) is a complicated process.
In this paper, we have developed a new mainlobe interference suppression method based on IAA spatial spectrum, INCM reconstruction, eigen-projection processing, and SINCM reconstruction, which annihilates the prior information restrictions existing in the previous methods for mainlobe interference suppression.
The mimetic method presented here uses the language of differential k-forms with k-cochains as their discrete counterpart, and the relations between them in terms of the mimetic operators: reduction, reconstruction and projection.
In this paper, a mainlobe coherent interference suppression algorithm is proposed based on IAA spatial spectrum, INCM reconstruction, eigen-projection processing, and sidelobe-interference-plus-noise covariance matrix (SINCM) reconstruction.
