Wavelets and Image Processing


Pedro J. Soto

Dr. J. Derado

Fall 2004




Since the advent of high-performance computing, digital imaging technologies and high-level languages, it has become not just feasible, but commonplace, to manipulate, analyze and transform what we see or can visualize. During the course of the sixteen weeks that comprise this semester, we have delved into image processing and analysis arming ourselves with wavelet theory, image manipulation tools and our own programming capabilities.

                While learning about wavelet theory and imaging technology, we set forth to empirically test these concepts using reference images such as the venerable Lena and ultimately attempting to analyze hurricane satellite images. Although the path at times was unknown—experimental and heavily based upon trial and error—we have reached some interesting and potentially promising results. While our ultimate goal was to be able to process galaxies and hurricanes, both of which are naturally occurring spirals whose composition is intrinsically different, we discovered along the way that accessible galaxy imaging was insufficiently detailed for the kind of analysis that we were seeking. Our hurricane analysis also suffered from the lack of access to a large enough library of hurricane satellite images. Nevertheless, some interesting results arose that warrant the continuation of this kind of research.

                Note that we have used Visual Studio .Net C# as our development platform, Adobe Photoshop 7.0 as our image manipulation tool. Due to the experimental nature of this project, the source code is far from optimized and will at times feel cumbersome.


1 The Haar Transform


We began by implementing the simplest kind of wavelet transform in our project,  the Haar Transform. For an image P of size we perform a two-pass filter. On our first pass, we process the image such that on a row k, for all pixels we have and for all pixels  we have . On our second pass, we perform the transpose of this operation, that is, we process the image column-by-column and store the averages and the differences on the result of the above process.


Sample code 1.1: First pass Haar transform


private double[,] firstPass(double[,] inputMatrix, int level, UpdateForm updateForm)


double[,] resultMatrix= new double[inputMatrix.GetLength(0),inputMatrix.GetLength(1)];

          int offset=inputMatrix.GetLength(0)/2;

          int    currentPixel = 0;

          double size         = inputMatrix.GetLength(0)*inputMatrix.GetLength(1);



          updateForm.LabelProgress="Processing Data";


          for(int y=0;y<inputMatrix.GetLength(1)/level;y++){                

                   for (int x=0;x<inputMatrix.GetLength(0)/2/level;x++){


                      resultMatrix[x,y] = (inputMatrix[2*x,y]+inputMatrix[2*x+1,y])/SQRT2;

                      resultMatrix[x+offset,y] = (inputMatrix[2*x,y]-


                      // Progress Bar Updating

                      if (currentPixel % 10000==0){

                         updateForm.updateProgress(((double)currentPixel*2/size)*100.0);                              }



          return resultMatrix;



Illustration 1.1 and 1.2:

Haar Transform Row-by Row and Column by Column



Illustration 1.3: Haar Transform End Result



The purpose of this transformation is to compress the energy into the first quadrant of the picture, and subsequent tests indicate that over 70% of the energy is contained in this first quadrant. The top-right quadrant stores the horizontal differences, the bottom-left the vertical ones and the bottom-right the diagonal ones. Note that in this last picture we experiences very little detail that is yet unexplained, but it seems to be a problem with loss of precision and correct color  rescaling.


Our program was redone to perform the Haar transform in a single step and the results are much better. In this one-pass transform, we handle four pixels at a time, calculating the averages and the details simultaneously. Also note that since we have been dividing by the square root of two, as opposed to the number of pixels (for the statistical mean), we have had to linearly scale the color range appropriately to avoid either clipping, or loss of precision or energy.


Illustration 1.4: One-pass Haar Transform



Note that in order to preserve the sign of the differences, we have been translating the difference results to the middle brightness value (128.) This way we can see that darker values are negative differences and lighter values are positive differences.


We also decided to perform some experiments with Lena: displaying only binary differences for a given threshold, and displaying the absolute value of the differences without the color offset.


Sample code 1.2: Binary/Threshold Haar transform and Absolute Value


private double[,] singlePassThreshold(double[,] inputMatrix, UpdateForm updateForm, double threshold, int level, bool binary)



   level = (int)Math.Pow(2.0,level-1);  


   double[,] resultMatrix= new double[inputMatrix.GetLength(0),inputMatrix.GetLength(1)];

                             int xOffset=inputMatrix.GetLength(0)/2/level;

                             int yOffset=inputMatrix.GetLength(1)/2/level;

                             int currentPixel = 0;

   double size         = inputMatrix.GetLength(0)*inputMatrix.GetLength(1);

   double multiplier   = double.Parse(textMultiplier.Text);



   updateForm.LabelProgress="Processing Data";


   for(int y=0;y<inputMatrix.GetLength(1);y++)


          for (int x=0;x<inputMatrix.GetLength(0);x++)


   if((y<inputMatrix.GetLength(1)/2/level) && (x<inputMatrix.GetLength(0)/2/level))



                resultMatrix[x,y] = normalizeColors(( inputMatrix[2*x,2*y] +

inputMatrix[2*x+1,2*y] + inputMatrix[2*x,2*y+1] + inputMatrix[2*x+1,2*y+1])/SQRT2,0,721);

double vertDiff = (-inputMatrix[2*x,2*y] - inputMatrix[2*x+1,2*y] +          

inputMatrix[2*x,2*y+1] + inputMatrix[2*x+1,2*y+1]);

double horzDiff = ( inputMatrix[2*x,2*y] - inputMatrix[2*x+1,2*y] +  

inputMatrix[2*x,2*y+1] - inputMatrix[2*x+1,2*y+1]);

                double diagDiff = (-inputMatrix[2*x,2*y] + inputMatrix[2*x+1,2*y] –

inputMatrix[2*x,2*y+1] + inputMatrix[2*x+1,2*y+1]);


      if (binary)


                   if (Math.Abs(vertDiff)>threshold)

                   {   resultMatrix[x+xOffset,y] = 255; }

                   if (Math.Abs(horzDiff)>threshold)

                   {  resultMatrix[x,y+yOffset] = 255;  }

                   if (Math.Abs(diagDiff)>threshold)

                   {  resultMatrix[x+xOffset,y+yOffset] = 255;}


                else {

                   resultMatrix[x+xOffset,y] = multiplier*Math.Abs(vertDiff);

                   resultMatrix[x,y+yOffset] = multiplier*Math.Abs(horzDiff);

                   resultMatrix[x+xOffset,y+yOffset] = multiplier*Math.Abs(diagDiff);


                if (currentPixel % 10000==0)

                {   updateForm.updateProgress(((double)currentPixel*2/size)*100.0);}  


             else {

                if ((x>=inputMatrix.GetLength(0)/level) || (y>=inputMatrix.GetLength(1)/level))

                {   resultMatrix[x,y]=inputMatrix[x,y]; }




   return resultMatrix;



Illustration 1.5: Binary Difference With Threshold 1.0


Illustration 1.6: Binary Difference, Threshold 5.0




Illustration 1.7: Binary Difference, Threshold 10.0



Illustration 1.8: Binary Difference, Threshold 20.0



Illustration 1.9: Absolute Value of Difference



This absolute value pass shows very little energy, so we process it in Photoshop to compress the brightness range and see more detail. The typical histogram of the absolute value differences for an image looks like the following graph and we proceed to compress this:



Illustration 1.10: Compressed Brightness Range in Absolute Value



Finally we implemented multiple level Haar transforms. In fact, we can notice that our Sample Code 1.1 includes an argument level that specifies which level of the Haar transform it will perform.


Illustration 1.11 : 3rd Level Haar Transform


Processing Hurricanes


At this point we turned our attention to using the tools that we had developed on geostationary satellite images.


Illustration 1.12 : Hurricane Ivan over the coast of Florida



Illustration 1.13 : Hurricaine Ivan, Haar 1st levelTransform



As we can see there is no apparent detail of the hurricane, but if we compress the color range of the differences in Photoshop, we get the following dramatic results:







Illustration 1.14: Ivan, Haar Transform, Exaggerated



This picture does indeed show substantial detail in the hurricane, particularly in the region of the eye.


Geostationary satellite image processing:


Now we will take a look at Ivan as it makes its way through the Caribbean, each time processing the Haar Transform in Photoshop to compress the range of brightness in the difference quadrants:





Illustration 1.15: Geostationary Progression



As we can see, detail is consistent across the pictures, with both the eye and arms being particularly detectable.


2. Daubechies Transform


The Daubchies coefficients that we used for this transform are:



This transform has the peculiarity that it works on four pixels at the same time, yet every iteration increments by two. Because of this “overlap”, it is necessary to “wrap around” such that when we reach the end of the picture and we are processing the last two pixels, we also use the first two pixels to make the total of four.


Sample Code 2.1: Daubechies First Pass


private double[,] daubFirstPass(double[,] inputMatrix, UpdateForm updateForm,int level,bool ignoreAverage)


   double[,] resultMatrix= new double[inputMatrix.GetLength(0),inputMatrix.GetLength(1)];

   int offset=inputMatrix.GetLength(0)/2;

   int    currentPixel = 0;


   for(int y=0;y<inputMatrix.GetLength(1);y++)


      for (int x=0;x<inputMatrix.GetLength(0)/2;x++)



          if (x < (inputMatrix.GetLength(0)/2)-1)


             resultMatrix[x,y] =


             resultMatrix[x+offset,y] = Double.Parse(textMultiplier.Text) * (

b1*inputMatrix[2*x,y] + b2*inputMatrix[2*x+1,y] + b3*inputMatrix[2*x+2,y] + b4*inputMatrix[2*x+3,y]);




             resultMatrix[x,y] = a3*inputMatrix[0,y] + a4*inputMatrix[1,y] +

a1*inputMatrix[2*x,y] + a2*inputMatrix[2*x+1,y];

resultMatrix[x+offset,y] = Double.Parse(textMultiplier.Text) * (


y]+b2*inputMatrix[2*x+1,y] );




   return resultMatrix;



*Note that in the above code there seem to be unused parameters. These are implemented in the final project.


The daubechies transform produced such a dark picture, that it was only useful after Photoshop processing.








Illustration 2.1: Daubechies Transform on Lena



The results on Lena are dramatic in the horizontal and vertical difference quadrants of the picture. We are seeing very direction-specific details show up, even so much so that the diagonals barely show the feathers in Lena’s hat. Also note the eye details that were picked up by this transform.


Although Lena was undoubtedly a success, we had mixed success when dealing with the hurricane pictures using Daubechies transform. Some of the hurricanes displayed fantastic detail whereas others had little detail to show, even after Photoshop post-processing.


Illustration 2.2 : Hurricane Ivan, Daubechies Transform



As we can see, there is very little eye or arm detail picked up in this case, even though the original photograph clearly shows both. However, when we processed a hurricane Frances photograph, our results were much different. The vertical and horizontal components were so clearly defined that the quadrants hardly looked like they had been produced from the same image. The image follows.

Illustration 2.3 : Hurricane Frances, Daubechies transform


3. 3 x 3 Transform


We finally arrive at the 3x3 transform. IN this transform, we process three pixels at a time, with no overlap, using the following coefficients:


Once again we handle the image row by row, and  then column by column. For all pixels

  we define

which are the energy-conserving averages. For the pixels we define

and finally, for pixels we have

The code was much more straightforward than the Daubechies transform, which required the “wrap-around”. I also made it general, accepting user input for the values a1,a2,a3,b1,b2,b3,c1,c2,c3 which will show in the next example as text fields.


Sample Code 3.1 : 3 x 3 Transform


private double[,] threeByThreeSecondPass(double[,] inputMatrix, UpdateForm updateForm)


   double[,] resultMatrix=new   


   int offset=(int)Math.Floor((double)inputMatrix.GetLength(1)/3.0);

   double c1_=double.Parse(c1.Text);

   double c2_=double.Parse(c2.Text);

   double c3_=double.Parse(c3.Text);

   double c4_=double.Parse(c4.Text);

   double c5_=double.Parse(c5.Text);

   double c6_=double.Parse(c6.Text);

   double c7_=double.Parse(c7.Text);

   double c8_=double.Parse(c8.Text);

   double c9_=double.Parse(c9.Text);


   for(int x=0;x<inputMatrix.GetLength(0);x++)


       for(int y=0;y<offset-3;y++)


          resultMatrix[x,y] = c1_*inputMatrix[x,3*y] + c2_*inputMatrix[x,3*y+1] +


          resultMatrix[x,y+offset]  = c4_*inputMatrix[x,3*y] +

c5_*inputMatrix[x,3*y+1] + c6_*inputMatrix[x,3*y+2];

resultMatrix[x,y+2*offset]= c7_*inputMatrix[x,3*y] +

c8_*inputMatrix[x,3*y+1] + c9_*inputMatrix[x,3*y+2];



   return resultMatrix;


Illustration 3.1 : 3 x3 Transform on Lena



These results are dramatic since they don’t include Photoshop post-processing. Most of the energy has been compressed into the averages as expected, but most detail can be found on the top-right hand and the bottom-left hand area, leaving 6 areas with little to no information. After post-processing, it is even more evident how much detail was preserved in these two areas.




Illustration 3.2 : Exaggerated 3 x 3 Transform:



We then immediately proceed to look at several hurricane photographs and compare the amount of detail preserved in the 3 x 3 transform, compared to the Daubechies transform.


Illustration 3.3 : Hurricane Ivan, Daubechies vs. 3 x 3



Even in these smaller sizes, the difference is staggering. Where Daubechies could barely represent the detail of the eye and arms of hurricane Ivan, the 3 x 3 transform has no problem. In a more cooperative picture, such as hurricane Frances, the difference is even more astounding.


Illustration 3.3 : Hurricane Frances, Daubechies vs. 3 x 3


We proceeded to analyze five hurricanes, whose 3 x 3 transforms can be found in the CD PowerPoint presentation that accompanies this document.


After producing 3 x 3 transforms of these hurricanes, we focused on the two areas that had most of the detail. For every hurricane, we select those two areas and make a new image comprised of only those two. We then look at the histogram of the resulting image.

Illustration 3.4 : Histogram of Hurricane Frances Detail



We now remove the most common coefficient value +/-9 as a threshold and we get the following results.


Illustration 3.5 : Histogram and Image of Hurricane Frances After +/-9 Threshold



We then tweak our threshold to be +/- 5 of the median and try our results  again


Illustration 3.6 : Frances Detail After +/-5 Threshold




Repeating this process for the other hurricanes we get these results.


Illustration 3.7 : Isabel Detail After +/-5 Threshold




Illustration 3.8 : Ivan Detail After +/-5 Threshold



Illustration 3.9 : Charley Detail After +/-5 Threshold



As we can see, some of these images simply have too much information at the median +/-5 threshold level.


4. Conclusions


                It was the original intent of the 3x3 transform to be able to find raw coefficients that would somehow be unique in the description of the hurricanes. Since the pictures vary in angle, location and quality, this wasn’t possible. The coefficient distribution seemed much more so due to the peculiarities of the image than to the intrinsic nature of the hurricane. We also lack the time to perform a thorough statistical analysis of the resulting coefficients (the raw coefficients, not once that they have been scaled and normalized for the sake of visualization.) However, it is important to note that in the final tweaking of the pixel histogram of the hurricane detail, it does show that it is possible to describe the arms and eye of a hurricane using very little information (energy.) Hurricane Frances was the best example of this, showing us a near-perfect contour of the hurricane at the +/-5 threshold level.  These results are promising, and it would be worthwhile to continue this research using the vectors corresponding to the wind velocities and the entire lifespan of a hurricane.

                It is also important to note that in order to proceed with deeper research, it would be vital to develop supporting image analysis tools in lieu of Photoshop that would complement the wavelet transform software that we have developed. There were many times were Photoshop, powerful as it is as an image manipulation program, could not give us the in-depth analysis that we needed. In the end, it is time that truncates our efforts but the results achieved with the 3 x 3 transforms seems to be very promising, and in being so, invite us to continue studying it.