We propose a direct reconstruction algorithm for Computed Tomography, based
on a local fusion of a few preliminary image estimates by means of a non-linear
fusion rule. One such rule is based on a signal denoising technique which is
spatially adaptive to the unknown local smoothness. Another, more powerful
fusion rule, is based on a neural network trained off-line with a high-quality
training set of images. Two types of linear reconstruction algorithms for the
preliminary images are employed for two different reconstruction tasks.