Run tensorflow model to achieve super resolution with opencv dnn.hpp - opencv

Question

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/imgproc.hpp"
#include<opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
#include <iostream>
using namespace std;
using namespace cv;
const size_t inWidth = 300;
const size_t inHeight = 300;
const float WHRatio = inWidth / (float)inHeight;
const char* classNames\[\] = { "background","face" };
void saveMat(cv::Mat inputMat,char* filename);
int main() {
String model = "opt_srcnn_vgg_test.pb";
dnn::Net net = cv::dnn::readNetFromTensorflow(model);
Mat img = imread("test.bmp");
Mat input;
img.copyTo(input);
cvtColor(input,input,CV_BGR2GRAY);//turn the rgb to gray
cout<<"load finish!"<<endl;
Mat inputBlob = dnn::blobFromImage(input, 1./255, Size(img.cols, img.rows));
imshow("input",input);
waitKey(0);
cout<<inputBlob.size;
net.setInput(inputBlob, "input");//set the network input,
cout<<"feed finish!"<<endl;
Mat pred = net.forward();
resize(pred,pred,Size(img.cols,img.rows));
imshow("result",pred);
waitKey(0);
return 0;
}
We tried to make opencv dnn use tensorflow trained .pb file to achieve super resolution. Our expected result is Figure 1, but we get the result of Figure 2 (Note: We determine that the .pb file is error-free). Why is this? Thank you!