We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by feed-forward neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.
@article{arxiv.1311.7251,
title = {Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks},
author = {Joseph Shtok and Michael Zibulevsky and Michael Elad},
journal= {arXiv preprint arXiv:1311.7251},
year = {2013}
}