2017 JRNL PP Kyong Hwan Jin - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
In this paper, we propose a novel deep convolutional
neural network (CNN)-based algorithm for solving ill-posed
inverse problems. Regularized iterative algorithms have emerged
as the standard approach to ill-posed inverse problems in the
past few decades. These methods produce excellent results, but
can be challenging to deploy in practice due to factors including
the high computational cost of the forward and adjoint operators
and the difficulty of hyperparameter selection. The starting point
of this paper is the observation that unrolled iterative methods
have the form of a CNN (filtering followed by pointwise nonlinearity)
when the normal operator (H?H, where H? is the
adjoint of the forward imaging operator, H) of the forwardmodel
is a convolution. Based on this observation, we propose using
direct inversion followed by a CNN to solve normal-convolutional
inverse problems. The direct inversion encapsulates the physical
model of the system, but leads to artifacts when the problem is
ill posed; the CNN combines multiresolution decomposition and
residual learning in order to learn to remove these artifacts while
preserving image structure. We demonstrate the performance
of the proposed network in sparse-view reconstruction (down
to 50 views) on parallel beam X-ray computed tomography in
synthetic phantoms as well as in real experimental sinograms.
The proposed network outperforms total variation-regularized
iterative reconstruction for the more realistic phantoms and
requires less than a second to reconstruct a 512 × 512 image
on the GPU.