Update README.md

Author: stevevanhooser

README.md

@@ -56,7 +56,7 @@ The principle behind DeepInterpolation is illustrated in the following figure fr
 ![Figure 1a from Lecoq et al. 2021](sampleData/Lecoq_et_al_2021_Fig1a.png). 
 --> 
 
-To predict the frame-of-interest ("predicted frame"), a deep learning network uses data from several preceeding and succeeding video frames. During training, the network is modified so that it produces better and better representations of the predicted frames over several datasets. During inference, the network produces predicted frames that are used in place of the original data. If the signal in the data is well predicted by the information in the preceeding and succeeding frames, then the inferred data contains a reconstruction of the original data where the usual noise that occurs independently on each frame is greatly reduced, because it is not predicted on average. The signal can be inferred with high quality in part because the network can average out the independent noise in the preceeding and succeeding frames to uncover the underlying signal just prior to and just after the signal in the predicted frame. 
+To predict the frame-of-interest ("predicted frame"), a deep learning network uses data from several preceeding and succeeding video frames. During training, the network is modified so that it produces better and better representations of the predicted frames over several datasets. During inference, the network produces predicted frames that are used in place of the original data. DeepInterpolation works well in situations where the signal in the data is well predicted by the information in the preceeding and succeeding frames. In these cases, the inferred data contains a good reconstruction of the underlying signal while the noise that occurs independently on each frame is greatly reduced, because the noise is not predicted on average.
 
 ### Installation
 🚧