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.

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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...