diff --git a/README.md b/README.md
index f0b1ba2..70a756e 100644
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@ class PixelUnShuffle(Module):
 # Abstract
 Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. 
 To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. 
-The proposed FFDNet works on downsampled subimages,achieving a good trade-off between inference speed and
+The proposed FFDNet works on downsampled subimages, achieving a good trade-off between inference speed and
 denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including
 
 - the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network,