README.md

---
license: bsd-3-clause
tags:
- Confocal Fluorescence Microscopy
- Image Super-resolution
- Deep Learning
- Benchmark
---

# [SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution](https://arxiv.org/pdf/xxxx.xxxxx.pdf)


by **Soufiane Belharbi<sup>1</sup>, Mara KM Whitford<sup>2,3</sup>, 
Phuong Hoang<sup>2</sup>, Shakeeb Murtaza<sup>1</sup>, Luke McCaffrey<sup>2,3,4</sup> Eric Granger<sup>1</sup>**


<sup>1</sup>  LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada
<br/>
<sup>2</sup>  Goodman Cancer Institute, McGill University, Montreal, Canada
<br/>
<sup>3</sup>  Dept. of Biochemistry, McGill University, Montreal, Canada
<br/>
<sup>4</sup>  Gerald Bronfman Dept. of Oncology, McGill University, Montreal,
Canada

<p align="center"><img src="patch-demo.png" alt="outline" width="80%"></p>

## ArXiv: [2402.00281](https://arxiv.org/pdf/2402.00281.pdf)
## Github: [https://github.com/sbelharbi/sr-caco-2](https://github.com/sbelharbi/sr-caco-2)


## Abstract
Confocal fluorescence microscopy is one of the most accessible and widely used 
imaging techniques for the study of biological processes at the cellular and 
subcellular levels. Scanning confocal microscopy allows the capture of 
high-quality images from thick three-dimensional (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.

**Code: Pytorch 2.0.0**

## Citation:
```
@article{belharbi24-sr-caco-2,
  title={SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution},
  author={Belharbi, S. and Hoang, P. and Whitford, M. and Murtaza, M. and McCaffrey, L. and Granger, E.},
  journal={CoRR},
  volume={abs/xxxx.xxxxx},
  year={2024}
}
```



## <a name="weights"> Pretrained weights (evaluation) </a>:
We provide the weights for all the models (135 models: 15 methods x 3 cells 
x 3 scales). Weights can be found at [Hugging Face](https://huggingface.co/sbelharbi/sr-caco-2) in the file [shared-trained-models.tar.gz](https://huggingface.co/sbelharbi/sr-caco-2/resolve/main/shared-trained-models.tar.gz?download=true).


The provided weights can be used to reproduce the reported results in the 
paper in the paper:
<p align="center"><img src="roi-perf.png" alt="roi performance" width="80%"></p>
<p align="center"><img src="full-img-perf.png" alt="full image performance" width="80%"></p>