1 research outputs found
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Traditional dictionary learning based CT reconstruction methods are
patch-based and the features learned with these methods often contain shifted
versions of the same features. To deal with these problems, the convolutional
sparse coding (CSC) has been proposed and introduced into various applications.
In this paper, inspired by the successful applications of CSC in the field of
signal processing, we propose a novel sparse-view CT reconstruction method
based on CSC with gradient regularization on feature maps. By directly working
on whole image, which need not to divide the image into overlapped patches like
dictionary learning based methods, the proposed method can maintain more
details and avoid the artifacts caused by patch aggregation. Experimental
results demonstrate that the proposed method has better performance than
several existing algorithms in both qualitative and quantitative aspects