How to remove the glare and brightness in an image (Image preprocessing)?

Question

I have an image

Is there any way of removing the bright white spots ? Please help thanks

Edit:

After operating with gaussian and then displaying using imagesc get the following output which clearly shows the bright red spots How do i get rid of them

Red Channel :

Green channel:

Blue channel:

Edit 2:

Defect detection using Gabor filter

Its Histogram :

How to calculate its appropriate threshold adaptivily.?

Answer 1

Let's assume glare portions are the only saturated areas in the image. Detection can be performed by thresholding the intensity (code in Mathematica):

saturated = Binarize[ColorConvert[img, "Grayscale"], .9]

Then we need only to replace the portions of the image around the saturation mask (enlarging the mask is done by the morphological function Dilation). Inpainting using texture synthesis (using the function Inpaint) seems to work well in this example, though I can not test it as input to your defect-detection algorithm:

Inpaint[img, Dilation[saturated, DiskMatrix[20]]]

Answer 2

This may be a bit of a simplistic answer, but could you just threshold? e.g.:

img = imread('daRNS.png');
imflat = img; 
imflat(img>150) = 150; 
imagesc(imflat)

results in:

It would obviously be better to select the threshold adaptively. For example you could look at the image histogram:

hist(double(img(:)),0:255)

and try to select an appropriate threshold based on that.

Answer 3

Without lighting information, it is difficult. However, if the shape of the object in the image is known, you could setup a shape template of the white glare (gaussian) and do a sliding window to find possible detection of glare (followed by color blending from adjacent area). Perceptually, we infer 3D shape from images using shading. If shape from shading is able to give the surface gradient, we could do a sliding window and check our glare template at each location.

After canny edge detection : -

Basically, the overlap ( max overlapping area ) between image #1 and #2 will be the defect.