# A recommender system to restore images with impulse noise

## TL;DR

This paper introduces a collaborative filtering recommender system for restoring images with impulse noise, utilizing a new color image representation and parameter tuning to improve image quality.

## Contribution

The paper proposes a novel collaborative filtering approach for impulse noise removal using a new color image representation and parameter analysis.

## Key findings

- Effective noise restoration demonstrated on a standard image database.
- Parameter tuning impacts bias and variance, influencing performance.
- Potential extension to image inpainting and super-resolution.

## Abstract

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.