updates readme

Readme.md

@@ -17,9 +17,9 @@ Examples can be found in the "example" folder, but they may require users to twe
 
 
  ## Image Processing
- Original images are all at least 3k x 3K. I downsample them into at most one side have 2048 with LANCZOS, then I randomly cut them into 256x256 as target  and use 128x128 with jpeg noise as input images. All input patches have at least 14 kb, and they are stored in SQLite with BLOB format. SQlite seems to have far [better performance](https://www.sqlite.org/intern-v-extern-blob.html) than file system for small objects.
+ Original images are all at least 3k x 3K. I downsample them  by LANCZOS so that  one side has at most 2048, then I randomly cut them into 256x256 patches as target  and use 128x128 with jpeg noise as input images. All input patches have at least 14 kb, and they are stored in SQLite with BLOB format. SQlite seems to have [better performance](https://www.sqlite.org/intern-v-extern-blob.html) than file system for small objects.
 
- Although convolutions can take in any sizes of images, the content of image matters. For real life images, small patches may maintain color,brightness, etc variances, but for digital drawn images, colors are added in block areas. A small patch may end up showing entirely one color, and the model has little to learn. 
+ Although convolutions can take in any sizes of images, the content of image matters. For real life images, small patches may maintain color,brightness, etc variances in small regions, but for digital drawn images, colors are added in block areas. A small patch may end up showing entirely one color, and the model has little to learn. 
 
  For example, the following two plots come from CARN and have the same settings, including initial parameters. Both training loss and ssim are lower for 64x64, but they perform worse in test time compared to 128x128.