Update README.md

README.md

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 license: bsd-3-clause
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+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)
+
+## 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>
+