Update README.md

README.md

@@ -33,29 +33,31 @@ Canada
 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
+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 
+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 datasets. 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 to evaluate the 
+performance of SISR methods on three different upscaling levels 
+(X2, x34, x8). SR-CACO-2 contains the human epithelial cell line Caco-2 
+(ATCC HTB-37), and it is composed of 2,200 unique images, captured with four 
+resolutions and three markers, that have been translated in the form of 9,937 
 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 
+we also provide benchmarking results for 16 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.
+limited success in producing high-resolution textures, indicating that SR-CACO-2 
+represents a challenging problem. The dataset is released under a Creative 
+Commons license (CC BY-NC-SA 4.0), and it can be accessed freely. Our dataset, 
+code and pretrained weights for SISR methods are publicly available: https://github.com/sbelharbi/sr-caco-2.
 
 **Code: Pytorch 2.0.0**