  Super-resolution (SR) methods have seen significant advances thanks to the
development of convolutional neural networks (CNNs). CNNs have been
successfully employed to improve the quality of endomicroscopy imaging. Yet,
the inherent limitation of research on SR in endomicroscopy remains the lack of
ground truth high-resolution (HR) images, commonly used for both supervised
training and reference-based image quality assessment (IQA). Therefore,
alternative methods, such as unsupervised SR are being explored. To address the
need for non-reference image quality improvement, we designed a novel zero-shot
super-resolution (ZSSR) approach that relies only on the endomicroscopy data to
be processed in a self-supervised manner without the need for ground-truth HR
images. We tailored the proposed pipeline to the idiosyncrasies of
endomicroscopy by introducing both: a physically-motivated Voronoi downscaling
kernel accounting for the endomicroscope's irregular fibre-based sampling
pattern, and realistic noise patterns. We also took advantage of video
sequences to exploit a sequence of images for self-supervised zero-shot image
quality improvement. We run ablation studies to assess our contribution in
regards to the downscaling kernel and noise simulation. We validate our
methodology on both synthetic and original data. Synthetic experiments were
assessed with reference-based IQA, while our results for original images were
evaluated in a user study conducted with both expert and non-expert observers.
The results demonstrated superior performance in image quality of ZSSR
reconstructions in comparison to the baseline method. The ZSSR is also
competitive when compared to supervised single-image SR, especially being the
preferred reconstruction technique by experts.
