diff --git a/wavelet2d/demo/imagedemo2.cpp b/wavelet2d/demo/imagedemo2.cpp
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+//============================================================================
+// Name        : imagedemo2.cpp
+// Author      : Rafat Hussain
+// Version     :
+// Copyright   : 
+// Description : Image Approximation using symmetric or periodic extension 2D DWT
+//============================================================================
+
+// IMPORTANT - Algorithm used to display Image is imprecise because of int 8 overflow issues
+// and it shouldn't be used to judge the performance of the DWT. The DWT and IDWT outputs
+// should be used for performance measurements. I have used maximum value rescaling to
+// solve overflow issues and , obviously, it is going to result in suboptimal performance but
+// it is good enough for demonstration purposes.
+
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <string>
+#include <complex>
+#include <cmath>
+#include <algorithm>
+#include "wavelet2s.h"
+#include "cv.h"
+#include "highgui.h"
+#include "cxcore.h"
+
+using namespace std;
+using namespace cv;
+
+void findthresh(vector<double> &vector1, int N, double& t){
+    sort(vector1.begin(), vector1.end(), greater<double>());
+    t = vector1.at(N-1);
+}
+
+void* maxval(vector<vector<double> > &arr, double &max){
+    max = 0;
+    for (unsigned int i =0; i < arr.size(); i++) {
+        for (unsigned int j =0; j < arr[0].size(); j++) {
+            if (max <= arr[i][j]){
+                max = arr[i][j];
+            }
+        }
+    }
+    return 0;
+}
+
+void* maxval1(vector<double> &arr, double &max){
+    max = 0;
+    for (unsigned int i =0; i < arr.size(); i++) {
+        if (max <= arr[i]){
+            max = arr[i];
+        }
+
+    }
+    return 0;
+}
+
+
+int main() {
+    IplImage* img = cvLoadImage("lena512.bmp");
+    if (!img){
+        cout << " Can't read Image. Try Different Format." << endl;
+        exit(1);
+    }
+    int height, width;
+    height = img->height;
+    width = img->width;
+    int nc = img->nChannels;
+    //   uchar* ptr2 =(uchar*) img->imageData;
+    int pix_depth = img->depth;
+    CvSize size;
+    size.width =width;
+    size.height=height;
+    cout << "depth" << pix_depth <<  "Channels" << nc << endl;
+
+
+    cvNamedWindow("Original Image", CV_WINDOW_AUTOSIZE);
+    cvShowImage("Original Image", img);
+    cvWaitKey();
+    cvDestroyWindow("Original Image");
+    cvSaveImage("orig.bmp",img);
+
+
+    int rows =(int) height;
+    int cols =(int) width;
+    Mat matimg(img);
+
+    vector<vector<double> > vec1(rows, vector<double>(cols));
+
+
+    int k =1;
+    for (int i=0; i < rows; i++) {
+        for (int j =0; j < cols; j++){
+            unsigned char temp;
+            temp = ((uchar*) matimg.data + i * matimg.step)[j  * matimg.elemSize() + k ];
+            vec1[i][j] = (double) temp;
+        }
+
+    }
+
+    string nm = "db2";
+    vector<double> l1,h1,l2,h2;
+    filtcoef(nm,l1,h1,l2,h2);
+    // unsigned int lf=l1.size();
+    //  int rows_n =(int) (rows+ J*(lf-1));
+    //  int cols_n =(int)  (cols + J * ( lf -1));
+
+    // Finding 2D DWT Transform of the image using symetric extension algorithm
+    // Extension is set to 0 (eg., int e = 0)
+
+    vector<int> length;
+    vector<double> output,flag;
+    int J =6;
+    dwt_2d(vec1,J,nm,output,flag,length);
+
+    double max;
+    vector<int> length2;
+    // This algorithm computes DWT of image of any given size. Together with convolution and
+    // subsampling operations it is clear that subsampled images are of different length than
+    // dyadic length images. In order to compute the "effective" size of DWT we do additional
+    // calculations.
+    dwt_output_dim_sym(length,length2,J);
+    // length2 is gives the integer vector that contains the size of subimages that will
+    // combine to form the displayed output image. The last two entries of length2 gives the
+    // size of DWT ( rows_n by cols_n)
+
+    int siz = length2.size();
+    int rows_n=length2[siz-2];
+    int cols_n = length2[siz-1];
+
+    vector<vector< double> > dwtdisp(rows_n, vector<double>(cols_n));
+    dispDWT(output,dwtdisp, length ,length2, J);
+
+    // dispDWT returns the 2D object dwtdisp which will be displayed using OPENCV's image
+    // handling functions
+
+    vector<vector<double> >  dwt_output= dwtdisp;
+
+    // Storing the DWT coefficients in two different vectors that will be used to approximate
+    // Image with two different sets of chosen coefficients.
+
+    vector<double> dwt_coef1;
+    vector<double> dwt_coef2;
+
+    dwt_coef1 = output;
+    dwt_coef2 = output;
+
+    maxval(dwt_output,max);// max value is needed to take care of overflow which happens because
+    // of convolution operations performed on unsigned 8 bit images
+
+    //Displaying Scaled Image
+    // Creating Image in OPENCV
+    IplImage *cvImg; // image used for output
+    CvSize imgSize; // size of output image
+
+    imgSize.width = cols_n;
+    imgSize.height = rows_n;
+
+    cvImg = cvCreateImage( imgSize, 8, 1 );
+    // dwt_hold is created to hold the dwt output as further operations need to be
+    // carried out on dwt_output in order to display scaled images.
+    vector<vector<double> > dwt_hold(rows_n, vector<double>( cols_n));
+    dwt_hold = dwt_output;
+    // Setting coefficients of created image to the scaled DWT output values
+    for (int i = 0; i < imgSize.height; i++ ) {
+        for (int j = 0; j < imgSize.width; j++ ){
+            if ( dwt_output[i][j] <= 0.0){
+                dwt_output[i][j] = 0.0;
+            }
+            if ( i <= (length2[0]) && j <= (length2[1]) ) {
+                ((uchar*)(cvImg->imageData + cvImg->widthStep*i))[j] =
+                        (char) ( (dwt_output[i][j] / max) * 255.0);
+            } else {
+                ((uchar*)(cvImg->imageData + cvImg->widthStep*i))[j] =
+                        (char) (dwt_output[i][j]) ;
+            }
+        }
+    }
+
+    cvNamedWindow( "DWT Image", 1 ); // creation of a visualisation window
+    cvShowImage( "DWT Image", cvImg ); // image visualisation
+    cvWaitKey();
+    cvDestroyWindow("DWT Image");
+    cvSaveImage("dwt.bmp",cvImg);
+
+     // Case 1 : Only 10% of the largest coefficients are considered
+
+              // Output is the 1D DWT vector
+
+              int n_coef1= int (output.size()/ 10);
+              cout << n_coef1 << endl;
+
+              // Finding Threshold Value corresponding to n_coef1
+
+              vector<double> temp1;
+                 cout << "size: " << (int) temp1.size() << "\n";
+                 cout << "capacity: " << (int) temp1.capacity() << "\n";
+                 cout << "max_size: " << (int) temp1.max_size() << "\n";
+                 for (unsigned int i =0; i < dwt_coef1.size(); i++) {
+                         double tempval = abs(dwt_coef1[i]);
+                         temp1.push_back(tempval);
+
+                 }
+
+                 double thresh1= 0.0;
+                 findthresh(temp1,n_coef1,thresh1);
+                 cout << "thresh" << thresh1 << endl;
+
+                 ofstream temp("temp.txt");
+                           for (unsigned int i =0; i < temp1.size(); i++){
+                               temp << temp1[i] << " " ;
+                           }
+
+                 // Reset coeffficients value depending on threshold value
+
+
+                 for (unsigned int i =0; i < dwt_coef1.size(); i++) {
+                           double temp = abs(dwt_coef1[i]);
+
+                           if (temp < thresh1){
+                               dwt_coef1.at(i)= 0.0;
+
+                           }
+
+                     }
+
+
+    // Finding IDWT
+
+    vector<vector<double> > idwt_output(rows, vector<double>(cols));
+
+    idwt_2d( dwt_coef1,flag, nm, idwt_output,length);
+
+    double max1;
+    maxval(idwt_output,max1);
+
+    //Displaying Reconstructed Image
+
+    IplImage *dvImg;
+    CvSize dvSize; // size of output image
+
+    dvSize.width = idwt_output[0].size();
+    dvSize.height = idwt_output.size();
+
+    cout << idwt_output.size() << idwt_output[0].size() << endl;
+    dvImg = cvCreateImage( dvSize, 8, 1 );
+
+    for (int i = 0; i < dvSize.height; i++ ){
+        for (int j = 0; j < dvSize.width; j++ ){
+                 if ( idwt_output[i][j] <= 0.0){
+                     idwt_output[i][j] = 0.0;
+                 }
+            ((uchar*)(dvImg->imageData + dvImg->widthStep*i))[j] =
+                    (char) ((idwt_output[i][j] / max1) * 255 ) ;
+        }
+    }
+
+    cvNamedWindow( "10% Coeff Reconstructed Image", 1 ); // creation of a visualisation window
+    cvShowImage( "10% Coeff Reconstructed Image", dvImg ); // image visualisation
+    cvWaitKey();
+    cvDestroyWindow("10% Coeff Reconstructed Image");
+    cvSaveImage("recon.bmp",dvImg);
+
+
+     // Case 2 : Only 2% of the largest coefficients are considered
+
+                  // Output is the 1D DWT vector
+
+                  int n_coef2= int (output.size()/ 50);
+                  cout << n_coef2 << endl;
+
+                  // Finding Threshold Value corresponding to n_coef1
+
+                  vector<double> temp2;
+
+                     for (unsigned int i =0; i < dwt_coef2.size(); i++) {
+                             double tempval = abs(dwt_coef2[i]);
+                             temp2.push_back(tempval);
+
+                     }
+
+                     double thresh2= 0.0;
+                     findthresh(temp2,n_coef2,thresh2);
+                     cout << "thresh" << thresh2 << endl;
+
+
+                     // Reset coeffficients value depending on threshold value
+
+
+                     for (unsigned int i =0; i < dwt_coef2.size(); i++) {
+                               double temp = abs(dwt_coef2[i]);
+
+                               if (temp < thresh2){
+                                   dwt_coef2.at(i)= 0.0;
+
+                               }
+
+                         }
+
+
+        // Finding IDWT
+
+        vector<vector<double> > idwt_output2(rows, vector<double>(cols));
+
+        idwt_2d( dwt_coef2,flag, nm, idwt_output2,length);
+
+        double max2;
+            maxval(idwt_output2,max2);
+
+
+
+        //Displaying Reconstructed Image
+
+        IplImage *dvImg2;
+        CvSize dvSize2; // size of output image
+
+        dvSize2.width = idwt_output2[0].size();
+        dvSize2.height = idwt_output2.size();
+
+        cout << idwt_output2.size() << idwt_output2[0].size() << endl;
+        dvImg2 = cvCreateImage( dvSize2, 8, 1 );
+
+        for (int i = 0; i < dvSize2.height; i++ ) {
+            for (int j = 0; j < dvSize2.width; j++ ) {
+                     if ( idwt_output2[i][j] <= 0.0){
+                         idwt_output2[i][j] = 0.0;
+                     }
+                ((uchar*)(dvImg2->imageData + dvImg2->widthStep*i))[j] =
+                        (char) ((idwt_output2[i][j]/ max2) * 255 ) ;
+            }
+        }
+
+        cvNamedWindow( "2% Coeff Reconstructed Image", 1 ); // creation of a visualisation window
+        cvShowImage( "2% Coeff Reconstructed Image", dvImg2 ); // image visualisation
+        cvWaitKey();
+        cvDestroyWindow("2% Coeff Reconstructed Image");
+        cvSaveImage("recon2.bmp",dvImg2);
+
+
+    return 0;
+}
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