De-blurring revisited with Wiener filter using OpenCV


In this post I continue to experiment with the de-blurring of images using the Wiener filter. For details on the Wiener filter, please look at my earlier post “Dabbling with Wiener filter using OpenCV”.  Thanks to Egli Simon, Switzerland for pointing out a bug in the earlier post which I have now fixed.  The Wiener filter attempts to de-blur by assuming that the source signal is convolved with a blur kernel in the presence of noise.   I have also included the blur kernel as estimated by E. Simon in the code. I am including the de-blurring with 3 different blur kernel radii and different values for the Wiener constant K.  While the de-blurring is still a long way off there is some success.

One of the reasons I have assumed a non-blind blur kernel and try to de-convolve with that. The Wiener filter tries to minimize the Mean Square Error (MSE)  which can be expressed as
e(f) = E[X(f) – X1(f)]^2                                – (1)

where e(f) is the Mean Square Error(MSE) in the frequency domain, X(f) is the original image and X1(f) is the estimated signal which we get by de-convolving the Wiener filter with the observed blurred image i.e. and E[] is the expectation

X1(f) = G(f) * Y(f)                                        -(2)
where G(f) is the Wiener de-convolution filter and Y(f) is the observed blurred image
Substituting (2) in (1) we get
e(f) = E[X(f) – G(f) *Y(f)]^2

If the above equation is solved we can effectively remove the blur.
Feel free to post comments, opinions or ideas.

Watch this space …
I will be back! Hasta la Vista!


Note: You can clone the code below from Git Hib – Another implementation of Weiner filter in OpenCV

The complete code is included below and should work as is

/*
============================================================================
Name : deblur_wiener.c
Author : Tinniam V Ganesh & Egli Simon
Version :
Copyright :
Description : Implementation of Wiener filter in OpenCV
============================================================================
*/

#include <stdio.h>
#include <stdlib.h>
#include “cxcore.h”
#include “cv.h”
#include “highgui.h”

#define kappa 10.0
#define rad 8

int main(int argc, char ** argv)
{
int height,width,step,channels,depth;
uchar* data1;

CvMat *dft_A;
CvMat *dft_B;

CvMat *dft_C;
IplImage* im;
IplImage* im1;

IplImage* image_ReB;
IplImage* image_ImB;

IplImage* image_ReC;
IplImage* image_ImC;
IplImage* complex_ImC;
CvScalar val;

IplImage* k_image_hdr;
int i,j,k;
char str[80];
FILE *fp;
fp = fopen(“test.txt”,”w+”);

int dft_M,dft_N;
int dft_M1,dft_N1;

CvMat* cvShowDFT1(IplImage*, int, int,char*);
void cvShowInvDFT1(IplImage*, CvMat*, int, int,char*);

im1 = cvLoadImage( “kutty-1.jpg”,1 );
cvNamedWindow(“Original-color”, 0);
cvShowImage(“Original-color”, im1);
im = cvLoadImage( “kutty-1.jpg”, CV_LOAD_IMAGE_GRAYSCALE );
if( !im )
return -1;

cvNamedWindow(“Original-gray”, 0);
cvShowImage(“Original-gray”, im);

// Create a random noise matrix
fp = fopen(“test.txt”,”w+”);
int val_noise[357*383];
for(i=0; i <im->height;i++){
for(j=0;j<im->width;j++){
fprintf(fp, “%d “,(383*i+j));
val_noise[383*i+j] = rand() % 128;
}
fprintf(fp, “\n”);
}

CvMat noise = cvMat(im->height,im->width, CV_8UC1,val_noise);

// Add the random noise matric to the image
cvAdd(im,&noise,im, 0);

cvNamedWindow(“Original + Noise”, 0);
cvShowImage(“Original + Noise”, im);

cvSmooth( im, im, CV_GAUSSIAN, 7, 7, 0.5, 0.5 );
cvNamedWindow(“Gaussian Smooth”, 0);
cvShowImage(“Gaussian Smooth”, im);

// Create a blur kernel
IplImage* k_image;
float r = rad;
float radius=((int)(r)*2+1)/2.0;

int rowLength=(int)(2*radius);
printf(“rowlength %d\n”,rowLength);
float kernels[rowLength*rowLength];
printf(“rowl: %i”,rowLength);
int norm=0; //Normalization factor
int x,y;
CvMat kernel;
for(x = 0; x < rowLength; x++)
for (y = 0; y < rowLength; y++)
if (sqrt((x – (int)(radius) ) * (x – (int)(radius) ) + (y – (int)(radius))* (y – (int)(radius))) <= (int)(radius))
norm++;
// Populate matrix
for (y = 0; y < rowLength; y++) //populate array with values
{
for (x = 0; x < rowLength; x++) {
if (sqrt((x – (int)(radius) ) * (x – (int)(radius) ) + (y – (int)(radius))
* (y – (int)(radius))) <= (int)(radius)) {
//kernels[y * rowLength + x] = 255;
kernels[y * rowLength + x] =1.0/norm;
printf(“%f “,1.0/norm);
}
else{
kernels[y * rowLength + x] =0;
}
}
}

/*for (i=0; i < rowLength; i++){
for(j=0;j < rowLength;j++){
printf(“%f “, kernels[i*rowLength +j]);
}
}*/

kernel= cvMat(rowLength, // number of rows
rowLength, // number of columns
CV_32FC1, // matrix data type
&kernels);
k_image_hdr = cvCreateImageHeader( cvSize(rowLength,rowLength), IPL_DEPTH_32F,1);
k_image = cvGetImage(&kernel,k_image_hdr);

height = k_image->height;
width = k_image->width;
step = k_image->widthStep/sizeof(float);
depth = k_image->depth;

channels = k_image->nChannels;
//data1 = (float *)(k_image->imageData);
data1 = (uchar *)(k_image->imageData);
cvNamedWindow(“blur kernel”, 0);
cvShowImage(“blur kernel”, k_image);

dft_M = cvGetOptimalDFTSize( im->height – 1 );
dft_N = cvGetOptimalDFTSize( im->width – 1 );

//dft_M1 = cvGetOptimalDFTSize( im->height+99 – 1 );
//dft_N1 = cvGetOptimalDFTSize( im->width+99 – 1 );

dft_M1 = cvGetOptimalDFTSize( im->height+3 – 1 );
dft_N1 = cvGetOptimalDFTSize( im->width+3 – 1 );

printf(“dft_N1=%d,dft_M1=%d\n”,dft_N1,dft_M1);

// Perform DFT of original image
dft_A = cvShowDFT1(im, dft_M1, dft_N1,”original”);
//Perform inverse (check)
//cvShowInvDFT1(im,dft_A,dft_M1,dft_N1, “original”); – Commented as it overwrites the DFT

// Perform DFT of kernel
dft_B = cvShowDFT1(k_image,dft_M1,dft_N1,”kernel”);
//Perform inverse of kernel (check)
//cvShowInvDFT1(k_image,dft_B,dft_M1,dft_N1, “kernel”);- Commented as it overwrites the DFT

// Multiply numerator with complex conjugate
dft_C = cvCreateMat( dft_M1, dft_N1, CV_64FC2 );

printf(“%d %d %d %d\n”,dft_M,dft_N,dft_M1,dft_N1);

// Multiply DFT(blurred image) * complex conjugate of blur kernel
cvMulSpectrums(dft_A,dft_B,dft_C,CV_DXT_MUL_CONJ);
//cvShowInvDFT1(im,dft_C,dft_M1,dft_N1,”blur1″);

// Split Fourier in real and imaginary parts
image_ReC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
complex_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 2);
printf(“%d %d %d %d\n”,dft_M,dft_N,dft_M1,dft_N1);

//cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );
cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );

// Compute A^2 + B^2 of denominator or blur kernel
image_ReB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);

// Split Real and imaginary parts
cvSplit( dft_B, image_ReB, image_ImB, 0, 0 );
cvPow( image_ReB, image_ReB, 2.0);
cvPow( image_ImB, image_ImB, 2.0);
cvAdd(image_ReB, image_ImB, image_ReB,0);
val = cvScalarAll(kappa);
cvAddS(image_ReB,val,image_ReB,0);

//Divide Numerator/A^2 + B^2
cvDiv(image_ReC, image_ReB, image_ReC, 1.0);
cvDiv(image_ImC, image_ReB, image_ImC, 1.0);

// Merge Real and complex parts
cvMerge(image_ReC, image_ImC, NULL, NULL, complex_ImC);
sprintf(str,”O/P Wiener – K=%6.4f rad=%d”,kappa,rad);

// Perform Inverse
cvShowInvDFT1(im, (CvMat *)complex_ImC,dft_M1,dft_N1,str);

cvWaitKey(-1);
return 0;
}

CvMat* cvShowDFT1(IplImage* im, int dft_M, int dft_N,char* src)
{
IplImage* realInput;
IplImage* imaginaryInput;
IplImage* complexInput;
CvMat* dft_A, tmp;
IplImage* image_Re;
IplImage* image_Im;
char str[80];
double m, M;
realInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
imaginaryInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
complexInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 2);
cvScale(im, realInput, 1.0, 0.0);
cvZero(imaginaryInput);
cvMerge(realInput, imaginaryInput, NULL, NULL, complexInput);

dft_A = cvCreateMat( dft_M, dft_N, CV_64FC2 );
image_Re = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);
image_Im = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);

// copy A to dft_A and pad dft_A with zeros
cvGetSubRect( dft_A, &tmp, cvRect(0,0, im->width, im->height));
cvCopy( complexInput, &tmp, NULL );
if( dft_A->cols > im->width )
{
cvGetSubRect( dft_A, &tmp, cvRect(im->width,0, dft_A->cols – im->width, im->height));
cvZero( &tmp );
}

// no need to pad bottom part of dft_A with zeros because of
// use nonzero_rows parameter in cvDFT() call below
cvDFT( dft_A, dft_A, CV_DXT_FORWARD, complexInput->height );
strcpy(str,”DFT -“);
strcat(str,src);
cvNamedWindow(str, 0);

// Split Fourier in real and imaginary parts
cvSplit( dft_A, image_Re, image_Im, 0, 0 );

// Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cvPow( image_Re, image_Re, 2.0);
cvPow( image_Im, image_Im, 2.0);
cvAdd( image_Re, image_Im, image_Re, NULL);
cvPow( image_Re, image_Re, 0.5 );

// Compute log(1 + Mag)
cvAddS( image_Re, cvScalarAll(1.0), image_Re, NULL ); // 1 + Mag
cvLog( image_Re, image_Re ); // log(1 + Mag)

cvMinMaxLoc(image_Re, &m, &M, NULL, NULL, NULL);
cvScale(image_Re, image_Re, 1.0/(M-m), 1.0*(-m)/(M-m));
cvShowImage(str, image_Re);
return(dft_A);
}

void cvShowInvDFT1(IplImage* im, CvMat* dft_A, int dft_M, int dft_N,char* src)
{
IplImage* realInput;
IplImage* imaginaryInput;
IplImage* complexInput;
IplImage * image_Re;
IplImage * image_Im;
double m, M;
char str[80];
realInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
imaginaryInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
complexInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 2);
image_Re = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);
image_Im = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);

//cvDFT( dft_A, dft_A, CV_DXT_INV_SCALE, complexInput->height );
cvDFT( dft_A, dft_A, CV_DXT_INV_SCALE, dft_M);
strcpy(str,”DFT INVERSE – “);
strcat(str,src);
cvNamedWindow(str, 0);

// Split Fourier in real and imaginary parts
cvSplit( dft_A, image_Re, image_Im, 0, 0 );

// Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cvPow( image_Re, image_Re, 2.0);
cvPow( image_Im, image_Im, 2.0);
cvAdd( image_Re, image_Im, image_Re, NULL);
cvPow( image_Re, image_Re, 0.5 );

// Compute log(1 + Mag)
cvAddS( image_Re, cvScalarAll(1.0), image_Re, NULL ); // 1 + Mag
cvLog( image_Re, image_Re ); // log(1 + Mag)

cvMinMaxLoc(image_Re, &m, &M, NULL, NULL, NULL);
cvScale(image_Re, image_Re, 1.0/(M-m), 1.0*(-m)/(M-m));
//cvCvtColor(image_Re, image, CV_GRAY2RGBA);
cvShowImage(str, image_Re);
}
See also
– De-blurring revisited with Wiener filter using OpenCV
–  Dabbling with Wiener filter using OpenCV
– Deblurring with OpenCV: Wiener filter reloaded
– Re-working the Lucy-Richardson Algorithm in OpenCV

You may also like
1.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
2.  Bend it like Bluemix, MongoDB with autoscaling – Part 1
3. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
4. A crime map of India in R: Crimes against women

Find me on Google+

Advertisements

14 thoughts on “De-blurring revisited with Wiener filter using OpenCV

  1. Pingback: Dabbling with Wiener filter using OpenCV « Giga thoughts …

  2. 1.
    Why is the deblurring filter applied to the output of cvSmooth??
    Should’nt it be applied to the output of the blur kernel that is created(and is being used to deblur) ??

    2.why is it :height + *3* -1 and not height – 1
    dft_M1 = cvGetOptimalDFTSize( im->height+3 – 1 );
    dft_N1 = cvGetOptimalDFTSize( im->width+3 – 1 );

    and i think you need to remove
    // Compute log(1 + Mag)
    cvAddS( image_Re, cvScalarAll(1.0), image_Re, NULL ); // 1 + Mag
    cvLog( image_Re, image_Re ); // log(1 + Mag)

    in the idft part.

    Thanks in advance,sorry if the questions are trivial

    • Sai – 1. I remember seeing somewhere that a Gaussian smooth needs to be applied after the noise function is performed.
      2. I think it should be im->height + rowLength -1 . We need to add the size of the blur kernel. The im->height + 3 -1 ( is code that was there with 3 x 3 blur kernl and was not removed. Will correct it.
      Do let me know your thoughts. Thanks for your comments.

  3. in the following code, you hadcode the width and height of your val_noise array, so the code does not work for image size different than 357*383….

    // Create a random noise matrix
    fp = fopen(“test.txt”,”w+”);
    int val_noise[357*383];
    for(i=0; i height;i++){
    for(j=0;jwidth;j++){
    fprintf(fp, “%d “,(383*i+j));
    val_noise[383*i+j] = rand() % 128;
    }
    fprintf(fp, “\n”);
    }

  4. newb in weiner but:

    val_noise[383*i+j] = rand() % 128; implies..you are adding a value between 0 and 128 to each point, which makes your image more luminant, but shouldn’t you add AND/OR substract value to have a real noise ? im afraid you induce a bias on luminance there…

    furthermore…is this really the noise we should use ? shouldnt we add random points in the image instead of adding random values to each pixel ?

  5. Pingback: Experiments with deblurring using OpenCV | Giga thoughts ...

  6. Pingback: Deblurring with OpenCV: Weiner filter reloaded | Giga thoughts ...

  7. Pingback: Re-working the Lucy Richardson algorithm in OpenCV | Giga thoughts ...

  8. Pingback: Hand detection through Haartraining: A hands-on approach | Giga thoughts …

  9. Pingback: Installing and using OpenCV with Visual Studio 2010 express | Giga thoughts …

  10. Pingback: Introducing cricket package yorkr: Part 2-Trapped leg before wicket! | Giga thoughts …

  11. Pingback: Neural Networks: The mechanics of backpropagation | Giga thoughts …

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s