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

• URL: http://arxiv.org/abs/2309.10511v2
• Date: Mon, 29 Apr 2024 09:38:20 GMT
• Title: Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
• Authors: Nadja Gruber, Johannes Schwab, Noémie Debroux, Nicolas Papadakis, Markus Haltmeier,
• Abstract summary: Self2Seg is a self-supervised method for the joint segmentation and denoising of a single image. In contrast to data-driven methods, Self2Seg segments an image into meaningful regions without any training database.
• Score: 5.980200432310941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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