Consumer-level digital cameras observe a single value
at each pixel, and full color images are reconstructed
using demosaicking. We improved the effectiveness
of standard demosaicking techniques by applying
loopy belief propagation, an iterative relaxation
technique from the probabilistic AI literature. The
enhanced algorithms show significant improvement in
mean-square error in both RGB and CIR color spaces.
We presented a refinement of histogram equalization which used both local and global information to remap the image greylevels. With only a small increase in computation time, we can improve contract enhancement over classical histogram equalization, while avoiding over-enhancement common with local histogram equalization.