  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.
