Image reconstruction from limited-angle projections using sparsifying operators
Image reconstruction from limited-angle projections has been a challenging problem for which an effective solution is constantly sought. This paper presents a novel method based on the concept of sparsifying operators. The idea is to construct a sparse model of the to-be-reconstructed image using a sparsifying operator and to estimate the model parameters using l0-minimization approximation from the partial k-space data computed from the limited projections. Thus, the missing k-space data can be recovered using the model and image is reconstructed by inverse Fourier transform. Experiments have shown that the proposed method can effectively recover the missing data and reconstruct images more accurately than the zero-filling (ZF) method and the total-variation (TV) regularized reconstruction method.