Single-Image based unsupervised joint segmentation and denoising
Nadja Gruber Johannes Schwab Noémie Debroux Nicolas Papadakis Markus Haltmeier
In this work, we develop an unsupervised method for the joint segmentation
and denoising of a single image. To this end, we combine the advantages of a
variational segmentation method with the power of a self-supervised,
single-image based deep learning approach. One major strength of our method
lies in the fact, that in contrast to data-driven methods, where huge amounts
of labeled samples are necessary, our model can segment an image into multiple
meaningful regions without any training database. Further, we introduce a novel
energy functional in which denoising and segmentation are coupled in a way that
both tasks benefit from each other. The limitations of existing single-image
based variational segmentation methods, which are not capable of dealing with
high noise or generic texture, are tackled by this specific combination with
self-supervised image denoising. We propose a unified optimisation strategy and
show that, especially for very noisy images available in microscopy, our
proposed joint approach outperforms its sequential counterpart as well as
alternative methods focused purely on denoising or segmentation. Another
comparison is conducted with a supervised deep learning approach designed for
the same application, highlighting the good performance of our approach.