Title: Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning,
Abstract: A number of image-processing problems can be formulated as optimization
problems. The objective function typically contains several terms specifically
designed for different purposes. Parameters in front of these terms are used to
control the relative weights among them. It is of critical importance to tune
these parameters, as quality of the solution depends on their values. Tuning
parameter is a relatively straightforward task for a human, as one can
intelligently determine the direction of parameter adjustment based on the
solution quality. Yet manual parameter tuning is not only tedious in many
cases, but becomes impractical when a number of parameters exist in a problem.
Aiming at solving this problem, this paper proposes an approach that employs
deep reinforcement learning to train a system that can automatically adjust
parameters in a human-like manner. We demonstrate our idea in an example
problem of optimization-based iterative CT reconstruction with a pixel-wise
total-variation regularization term. We set up a parameter tuning policy
network (PTPN), which maps an CT image patch to an output that specifies the
direction and amplitude by which the parameter at the patch center is adjusted.
We train the PTPN via an end-to-end reinforcement learning procedure. We
demonstrate that under the guidance of the trained PTPN for parameter tuning at
each pixel, reconstructed CT images attain quality similar or better than in
those reconstructed with manually tuned parameters.