  Confocal fluorescence microscopy is one of the most accessible and widely
used imaging techniques for the study of biological processes. Scanning
confocal microscopy allows the capture of high-quality images from 3D samples,
yet suffers from well-known limitations such as photobleaching and
phototoxicity of specimens caused by intense light exposure, which limits its
use in some applications, especially for living cells. Cellular damage can be
alleviated by changing imaging parameters to reduce light exposure, often at
the expense of image quality. Machine/deep learning methods for single-image
super-resolution (SISR) can be applied to restore image quality by upscaling
lower-resolution (LR) images to produce high-resolution images (HR). These SISR
methods have been successfully applied to photo-realistic images due partly to
the abundance of publicly available data. In contrast, the lack of publicly
available data partly limits their application and success in scanning confocal
microscopy. In this paper, we introduce a large scanning confocal microscopy
dataset named SR-CACO-2 that is comprised of low- and high-resolution image
pairs marked for three different fluorescent markers. It allows the evaluation
of performance of SISR methods on three different upscaling levels (X2, X4,
X8). SR-CACO-2 contains the human epithelial cell line Caco-2 (ATCC HTB-37),
and it is composed of 22 tiles that have been translated in the form of 9,937
image patches for experiments with SISR methods. Given the new SR-CACO-2
dataset, we also provide benchmarking results for 15 state-of-the-art methods
that are representative of the main SISR families. Results show that these
methods have limited success in producing high-resolution textures, indicating
that SR-CACO-2 represents a challenging problem. Our dataset, code and
pretrained weights are available: https://github.com/sbelharbi/sr-caco-2.
