How to Remove a Glare / Clipped Brightness from an Image?

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.

Answer 4

My opinion is that this is a machine vision problem in which you should be controlling the lighting and have a good idea of the maximum brightness of a non-glare pixel brightness in the image. Defect detection is generally a machine vision problem rather than a computer vision problem.

What we see as a result of lighting is an addition of specular and diffuse reflections of light (plus some emittance but its negligible here).

The specular component is the glare, on shiny surface like this apple, it is much more than the diffuse reflection (>10x)

This means that if you setup your lighting, gain and exposure prior to this, on a diffuse surface, you can be sure that nothing will be even close to saturated. So using a fixed threshold is actually the preferred solution here, as long as you've proven with enough data that "no pixels not containing glare" would be above the threshold. In essence you are setting up the lighting conditions, and camera parameters such that classification of a pixel becomes trivial, in this case performed by a simple threshold, rather than a more complex machine learned function of pixels around it.

I like "vini"'s approach, no real need to show the RGB planes. Just a simple grayscale threshold would actually work here.

1- you design the lighting conditions, not ambient

2- make the classification job extremely trivial (thresholding)

3- measure the feature

4- compare to tolerance

Answer 5

Convert to lab colour space first, mapminmax, then use the first luminosity channel. That reduces the colour issues. Then use some thresholding on the top 80% brightest pixel. Check and test for a dip in the histogram, the best threshold is near the bottom of the dip. If there is no local min in this region your image probably has minimal reflections...... Martin