# Reducing Anomaly Detection in Images to Detection in Noise

## TL;DR

This paper proposes a novel unsupervised method for anomaly detection in images by transforming the problem into detecting anomalies in residual noise images, simplifying background modeling and enabling rigorous thresholding.

## Contribution

It introduces a noise-based anomaly detection approach that reduces complex background modeling to residual noise analysis, applicable to arbitrary images.

## Key findings

- Effective in detecting anomalies across various image backgrounds
- Enables rigorous threshold setting using a contrario detection theory
- Unsupervised method applicable to diverse image types

## Abstract

Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our approach is therefore unsupervised and works on arbitrary images.