  We build a collaborative filtering recommender system to restore images with
impulse noise for which the noisy pixels have been previously identified. We
define this recommender system in terms of a new color image representation
using three matrices that depend on the noise-free pixels of the image to
restore, and two parameters: $k$, the number of features; and $\lambda$, the
regularization factor. We perform experiments on a well known image database to
test our algorithm and we provide image quality statistics for the results
obtained. We discuss the roles of bias and variance in the performance of our
algorithm as determined by the values of $k$ and $\lambda$, and provide
guidance on how to choose the values of these parameters. Finally, we discuss
the possibility of using our collaborative filtering recommender system to
perform image inpainting and super-resolution.
