# Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising

arXiv: https://arxiv.org/abs/2309.10511 · HTML: https://mlsanity.com/paper/2309.10511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach](https://mlsanity.com/paper/1706.04284/) (2017)
- [Noise2Fast: Fast Self-Supervised Single Image Blind Denoising](https://mlsanity.com/paper/2108.10209/) (2021)
- [DenoiSeg: Joint Denoising and Segmentation](https://mlsanity.com/paper/2005.02987/) (2020)
- [Leveraging Self-supervised Denoising for Image Segmentation](https://mlsanity.com/paper/1911.12239/) (2019)
- [Convex variational methods for multiclass data segmentation on graphs](https://mlsanity.com/paper/1605.01443/) (2016)
- [Variational Multi-Phase Segmentation using High-Dimensional Local Features](https://mlsanity.com/paper/1902.09863/) (2019)
- [Variational Image Segmentation Model Coupled with Image Restoration Achievements](https://mlsanity.com/paper/1405.2128/) (2014)
- [Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging](https://mlsanity.com/paper/2202.04680/) (2022)

## Built on by (most cited first, 1 of 1)

- [On Inverse Problems, Parameter Estimation, and Domain Generalization](https://mlsanity.com/paper/2506.06024/) (2025)

## Abstract (arXiv)

We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with self-supervised deep learning. One major benefit of our method lies in the fact, that in contrast to data-driven methods, where huge amounts of labeled samples are necessary, Self2Seg segments an image into meaningful regions without any training database. Moreover, we demonstrate that self-supervised denoising itself is significantly improved through the region-specific learning of Self2Seg. Therefore, we introduce a novel self-supervised energy functional in which denoising and segmentation are coupled in a way that both tasks benefit from each other. We propose a unified optimisation strategy and numerically show that for noisy microscopy images our proposed joint approach outperforms its sequential counterpart as well as alternative methods focused purely on denoising or segmentation.