Illumination normalization in OpenCV

Question

I am working on a face recognition project. I have pictures with different lighting so I need to do illumination normalization. I read a paper which which claims to do illumination normalization. The paper describe the following function and values.

1- gamma correction with gamma = 0.2
2- Difference of Gaussian (DOG) filtering with (sigma0 = 1, sigma1 =2)
3- contrast equalization (truncation threshold of 10 and compressive component 0.1 is used in the paper)

I use CvPow for gamma correction, CvSmooth for DoG and Threshold() with truncate (I don't know how to specify the compression component) but I didn't get the exact image. I used histogram equalization for contrast equalization.

If someone has done it before or has any idea??

Link to the paper: http://lear.inrialpes.fr/pubs/2007/TT07/Tan-amfg07a.pdf

The code is below: (Python code of Peb Aryan converted to JAVACV)

public static IplImage preprocessImg(IplImage img)
{
    IplImage gf = cvCreateImage(cvSize(img.width(),img.height()),IPL_DEPTH_32F, 1 );
    IplImage gr = IplImage.create(img.width(),img.height(), IPL_DEPTH_8U, 1);
    IplImage tr = IplImage.create(img.width(),img.height(), IPL_DEPTH_8U, 1);

    IplImage b1 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    IplImage b2 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    IplImage b3 = IplImage.create(img.width(),img.height(),IPL_DEPTH_32F, 1 );
    CvArr mask = IplImage.create(0,0,IPL_DEPTH_8U, 1 );

    cvCvtColor(img, gr, CV_BGR2GRAY); 
    gamma(gr,gr,gf);

    cvSmooth(gf,b1,CV_GAUSSIAN, 1);
    cvSmooth(gf,b2,CV_GAUSSIAN,23);
    cvSub(b1,b2,b2,mask);         
    cvConvertScale(b2,gr,127,127);
    cvEqualizeHist(gr, gr);

    //cvThreshold(gr,tr,255,0,CV_THRESH_TRUNC);

    return gr;
}

public static void gamma(IplImage src,IplImage dst, IplImage temp)
{
    cvConvertScale(src,temp, 1.0/255,0);
    cvPow(temp, temp, 0.2);
    cvConvertScale(temp, dst, 255,0);
}

Here is the result of my attempt:

And the reference from the paper:

Answer 1

Don't know if it's too late for you.

In the original paper, DoG was performed by a given sigma, here your radius(23) it too big. Try radius = 7 and radius = 1. About the equalization step, it's different from the paper. you need implement one by yourself.

BTW: some basic functions like cvSmooth was not implemented right for your application. You probably need to implement by yourself to get a better result.