摘要：We develop a
hyperparameter inference method for image
reconstruction from Radon transform
which often appears in the computed tomography, in the manner of
Bayesian inference. Hyperparameters are often introduced in
Bayesian inference to control the strength ratio between prior
information and the fidelity to the observation. Since the quality
of the reconstructed image is controlled by the estimation
accuracy of these hyperparameters, we apply Bayesian inference
into the filtered back-projection (FBP) reconstruction method with
hyperparameters inference and demonstrate that the estimated
hyperparameters can adapt to the noise level in the observation
automatically. In the computer simulation, at first, we show that our
algorithm works well in the model framework environment, that
is, observation noise is an additive white Gaussian noise case. Then,
we also show that our algorithm works well in the more realistic
environment, that is, observation noise is Poissonian noise case.
After that, we demonstrate an application for the real chest CT
image reconstruction under the Gaussian and Poissonian observation
noises.
