diff --git a/trunk/RTCSA_SS/02Background.tex b/trunk/RTCSA_SS/02Background.tex
index e71958b..1e99cfe 100644
--- a/trunk/RTCSA_SS/02Background.tex
+++ b/trunk/RTCSA_SS/02Background.tex
@@ -11,7 +11,7 @@ C. Dong et al.~\cite{ChaoDong16} proposed a deep learning method for single imag
 super-resolution (SR). Their method learns how to directly map the low-resolution
 image to high-resolution image. They show that the traditional sparse coding
 based SR methods can be reformulated into a deep convolutional neural network.
-SRCNN consists three operations.%, illustrated in Figure~\ref{fig:SRCNN_model}.
+SRCNN consists three operations.
 The first layer of SRCNN model extracts the patches from low-resolution image.
 The second layer maps the patches from low-resolution to high-resolution. The
 third layer will reconstruct the high-resolution image by the high-resolution
@@ -27,7 +27,7 @@ patches.
 
 \subsection{Thermal cameras}
 In this work, we use two different resolution thermal cameras to play the role
-of low-resolution and high-resolution camera. For low-resolution camera, we use
+of low-resolution and high-resolution cameras. For low-resolution camera, we use
 Grid-EYE thermal camera. Grid-EYE is a thermal camera that can output
 $8 \times 8$ pixels thermal data with $2.5^\circ C$ accuracy and $0.25^\circ C$
 resolution at $10$ fps. For the high-resolution one, we use Lepton 3. The