# Non-linear aggregation of filters to improve image denoising

## TL;DR

This paper presents a new non-linear filter aggregation method for image denoising that combines multiple filters based on a novel pixel proximity metric, resulting in improved denoising performance supported by theoretical and empirical evidence.

## Contribution

The paper introduces a novel non-linear aggregation scheme for image denoising that outperforms individual filters and provides theoretical bounds for its effectiveness.

## Key findings

- Aggregated filters outperform individual filters in denoising tasks.
- The proposed method is supported by theoretical performance bounds.
- Numerical experiments demonstrate significant denoising improvements.

## Abstract

We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.