2023/09/19 by Nadja Gruber, Gruber, Nadja, Johannes Schwab +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #65K10 #68T07 #68U05 #68U10 #68U15 #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Photoacoustic and Ultrasonic Imaging
paper · pdf · doi:10.48550/arxiv.2309.10511
openalex publication_date 2023/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.