We introduce a post-processing approach to improve the quality of CT reconstructed images. The scheme is
adapted from the resolution-synthesis (RS)1 interpolation algorithm. In this approach, we consider the input
image, scanned at a particular dose level, as a degraded version of a high quality image scanned at a high
dose level. Image enhancement is achieved by predicting the high quality image by classification based linear
regression. To improve the robustness of our scheme, we also apply the minimum description length principle
to determine the optimal number of predictors to use in the scheme, and the ridge regression to regularize the
design of the predictors. Experimental results show that our scheme is effective in reducing the noise in images
reconstructed from filtered back projection without significant loss of image details. Alternatively, our scheme
can also be applied to reduce dose while maintaining image quality at an acceptable level.
We construct a hierarchical image grammar model based on stochastic grammars and apply it to document images. An efficient maximum a posteriori probability estimation algorithm for this model produces accurate segmentations of document images and classifications of image parts.