Reconstruction from limited-angle projections based on spectrum analysis

This paper proposes a sparse representation of an

image using discrete functions. A function is defined

as the product of a Kronecker delta function and a step

function. Based on the sparse representation, we have developed

a novel and effective method for reconstructing an image from

limited-angle projections. The method first estimates the parameters

of the sparse representation from the incomplete projection

data, and then directly calculates the image to be reconstructed.

Experiments have shown that the proposed method can effectively

recover the missing data and reconstruct images more accurately

than the total-variation (TV) regularized reconstruction method.