Add update to dataset curator and source information.

README.md

@@ -52,7 +52,6 @@ task_ids:
 - **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k
 - **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf
 - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
-- **Point of Contact**: radu.timofte@vision.ee.ethz.ch
 
 ### Dataset Summary
 
@@ -139,21 +138,44 @@ The data fields are the same among all splits.
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+**Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of 
+the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most 
+common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of 
+12 pixels on both axes. Most of the crawled images were originally above 20M pixels. 
+The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions 
+(like blur and color shifts).
+
+**Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely 
+shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr, 
+Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity
+for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments 
+(cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions.
+
+**Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG 
+compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with 
+bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings. 
+
+The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good 
+balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per 
+image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of 
+bicubic, ANR and A+ methods. 
+
+Only the 800 train and 100 validation images are included in this dataset.
 
 #### Who are the source language producers?
 
-[More Information Needed]
+The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality,
+to the diversity of sources (sites and cameras), to the image contents and to the copyrights. 
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+No annotations.
 
 #### Who are the annotators?
 
-[More Information Needed]
+No annotators.
 
 ### Personal and Sensitive Information
 
@@ -179,7 +201,7 @@ immediately.
 
 ### Dataset Curators
 
-[More Information Needed]
+- **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/)
 
 ### Licensing Information