How to improve image quality in Matlab

Question

I\'m building an \"Optical Character Recognition\" system.

so far the system is capable to identify licence plates in good quality without any noise.

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Answer 1

We can try to correct for lighting effect by fitting a linear plane over the image intensities, which will approximate the average level across the image. By subtracting this shading plane from the original image, we can attempt to normalize lighting conditions across the image.

For color RGB images, simply repeat the process on each channel separately, or even apply it to a different colorspace (HSV, Lab*, etc...)

Here is a sample implementation:

function img = correctLighting(img, method)
    if nargin<2, method='rgb'; end
    switch lower(method)
        case 'rgb'
            %# process R,G,B channels separately
            for i=1:size(img,3)
                img(:,:,i) = LinearShading( img(:,:,i) );
            end
        case 'hsv'
            %# process intensity component of HSV, then convert back to RGB
            HSV = rgb2hsv(img);
            HSV(:,:,3) = LinearShading( HSV(:,:,3) );
            img = hsv2rgb(HSV);
        case 'lab'
            %# process luminosity layer of L*a*b*, then convert back to RGB
            LAB = applycform(img, makecform('srgb2lab'));
            LAB(:,:,1) = LinearShading( LAB(:,:,1) ./ 100 ) * 100;
            img = applycform(LAB, makecform('lab2srgb'));
    end
end

function I = LinearShading(I)
    %# create X-/Y-coordinate values for each pixel
    [h,w] = size(I);
    [X Y] = meshgrid(1:w,1:h);

    %# fit a linear plane over 3D points [X Y Z], Z is the pixel intensities
    coeff = [X(:) Y(:) ones(w*h,1)] \ I(:);

    %# compute shading plane
    shading = coeff(1).*X + coeff(2).*Y + coeff(3);

    %# subtract shading from image
    I = I - shading;

    %# normalize to the entire [0,1] range
    I = ( I - min(I(:)) ) ./ range(I(:));
end

Now lets test it on the given image:

img = im2double( imread('http://i.stack.imgur.com/JmHKJ.jpg') );
subplot(411), imshow(img)
subplot(412), imshow( correctLighting(img,'rgb') )
subplot(413), imshow( correctLighting(img,'hsv') )
subplot(414), imshow( correctLighting(img,'lab') )

The difference is subtle, but it might improve the results of further image processing and OCR task.

---

EDIT: Here is some results I obtained by applying other contrast-enhancement techniques IMADJUST, HISTEQ, ADAPTHISTEQ on the different colorspaces in the same manner as above:

Remember you have to fine-tune any parameter to fit your image...

Answer 2

It looks like your question has been more or less answered already (see d00b's comment); however, here are a few basic image processing tips that might help you here.

First, you could try a simple imadjust. This simply maps the pixel intensities to a "better" value, which often increases the contrast (making it easier to view/read). I have had a lot of success with it in my work. It is easy to use too! I think its worth a shot.

Also, this looks promising if you simply want a higher resolution image.

Enjoy the "pleasure" of image-processing in MATLAB!

Good luck,

tylerthemiler

P.S. If you are flattening the image to binary tho, you are most likely ruining the image to start with, so don't do that if you can avoid it!

Answer 3

As you only want to find digits (of which there are only 10), you can use cross-correlation. For this you would Fourier transform the picture of the plate. You also Fourier transform a pattern you want to match a good representation of a picture of the digit 1. Then you multiply in fourier space and inversely Fourier transform the result.

In the final cross-correlation, you will see pronounced peaks, where the pattern overlaps nicely with your image.

You do this 10 times and know where each digit is. Note that you must correct the tilt before you do the cross correlation.

This method has the advantage that you don't have to threshold your image.

There are certainly much more sophisticated algorithms in the literature for assigning number plates. One could for example use Bayes theory to estimate which digit would most likely occur (this helps a lot if you already have a databases of possible numbers).