Add update results.

README.md

@@ -4,7 +4,7 @@ tags:
 - super-image
 - image-super-resolution
 datasets:
-- div2k
+- eugenesiow/Div2k
 metrics:
 - pnsr
 - ssim
@@ -14,7 +14,7 @@ MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images,
 
 The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
 
-![Comparing Bicubic upscaling against the models x2 upscaling on Set5 Image 4](images/msrn_4_4_compare.png "Comparing Bicubic upscaling against the models x2 upscaling on Set5 Image 4")
+![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4](images/msrn_4_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4")
 ## Model description
 The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
 ## Intended uses & limitations
@@ -110,23 +110,24 @@ The results columns below are represented below as `PSNR/SSIM`. They are compare
 |Set5  	        |4x  	    |28.42/0.8101  	    |**32.19/0.8951**               |
 |Set14  	    |2x         |30.22/0.8683  	    |  	    |
 |Set14  	    |3x         |27.53/0.7737  	    |  	    |
-|Set14  	    |4x         |25.99/0.7023  	    |**28.67/0.7833**  	            |
+|Set14  	    |4x         |25.99/0.7023  	    |**28.78/0.7862**  	            |
 |BSD100  	    |2x  	    |29.55/0.8425  	    |  	    |
 |BSD100  	    |3x  	    |27.20/0.7382  	    |  	    |
-|BSD100  	    |4x  	    |25.96/0.6672  	    |**27.63/0.7374**  	            |
+|BSD100  	    |4x  	    |25.96/0.6672  	    |**28.53/0.7657**  	            |
 |Urban100  	    |2x  	    |26.66/0.8408  	    |  	    |
 |Urban100  	    |3x  	    |  	                | 	    |
 |Urban100  	    |4x  	    |23.14/0.6573  	    |**26.12/0.7866**  	            |
 
-![Comparing Bicubic upscaling against the models x2 upscaling on Set5 Image 2](images/msrn_2_4_compare.png "Comparing Bicubic upscaling against the models x2 upscaling on Set5 Image 2")
+![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2](images/msrn_2_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2")
 
 ## BibTeX entry and citation info
 ```bibtex
-@InProceedings{Li_2018_ECCV,
-    author = {Li, Juncheng and Fang, Faming and Mei, Kangfu and Zhang, Guixu},
-    title = {Multi-scale Residual Network for Image Super-Resolution},
-    booktitle = {The European Conference on Computer Vision (ECCV)},
-    month = {September},
-    year = {2018}
-}
+@InProceedings{Agustsson_2017_CVPR_Workshops,
+    author = {Agustsson, Eirikur and Timofte, Radu},
+    title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
+    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
+    month = {July},
+    year = {2017}
+} 
 ```