# Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

## TL;DR

Probabilistic Noise2Void (PN2V) is a self-supervised CNN-based denoising method that predicts per-pixel intensity distributions, enabling effective unsupervised image denoising across various noise conditions.

## Contribution

PN2V introduces a probabilistic framework for self-supervised denoising by training CNNs to predict pixel-wise intensity distributions, improving upon existing methods.

## Key findings

- Achieves competitive denoising results on microscopy datasets.
- Works effectively across diverse noise regimes.
- Outperforms traditional self-supervised methods in certain scenarios.

## Abstract

Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods such as Noise2Void~(N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods.