초록 ▼

The method identifies a Pixon element, which is a fundamental and indivisible unit of information, and a Pixon basis, which is the set of possible functions from which the Pixon elements are selected. The actual Pixon elements selected from this basis during the reconstruction process represents the

The method identifies a Pixon element, which is a fundamental and indivisible unit of information, and a Pixon basis, which is the set of possible functions from which the Pixon elements are selected. The actual Pixon elements selected from this basis during the reconstruction process represents the smallest number of such units required to fit the data and representing the minimum number of parameters necessary to specify the image. The Pixon kernels can have arbitrary properties (e.g., shape size, and/or position) as needed to best fit the data.

대표청구항 ▼

1. In an image reconstruction process, a method for generating a kernel map for modeling an image wherein the image corresponds to a plurality of input data points on an image grid comprising a plurality of grid points, the method comprising:(a) defining a plurality of different kernel sets, each di

1. In an image reconstruction process, a method for generating a kernel map for modeling an image wherein the image corresponds to a plurality of input data points on an image grid comprising a plurality of grid points, the method comprising:(a) defining a plurality of different kernel sets, each different kernel set comprising a plurality of identical kernels covering the image grid, wherein each kernel within a kernel set covers a portion of the image grid encompassing a portion of the plurality of data points so that the plurality of kernels of a kernel set covers the image grid, and wherein the kernels within each different kernel set have a different size and encompass a different quantity of grid points compared to the kernels within other different kernel sets; (b) convolving the plurality of input data points with the plurality of different kernel sets; (c) determining a signal-to-noise ratio and a goodness-of-fit for each kernel by mapping the kernel into data space and comparing the mapped kernel to the portion of the plurality of input data points within the kernel (d) comparing the signal-to-noise ratio and the goodness-of-fit for each kernel against predetermined pass criteria; (e) selecting at each location within the grid an optimal kernel from the plurality of different kernel sets wherein the optimal kernel is the broadest kernel that meets the predetermined pass criteria in that location; and (f) generating a kernel map comprising a plurality of optimal kernels, wherein each optimal kernel covers a location within the grid so that the grid is covered by the plurality of optimal kernels.