Activity 11 – Color Image Processing

This activity is mainly about proper white balancing of images under certain lighting conditions and also about understanding how incorrect white balanced images be corrected. Color in images is represented by the RGB channels. Mathematically, the color perceived by the camera is the sum of the product of the object reflectance, source illumination and the camera spectral sensitivity channels for RGB. It can be presented using:

where

K is the white balancing constant and all of these parameters constitute to the image taken by the camera.

Shown above are four images with the two outmost images as white patches corresponding to the near images. Each white patch is used to do automatic white balancing in the two images. These two images are different because they are taken at different NA and shutter speed values that is why the image at the left looks darker.

Two most popular ways of obtaining auto white balancing is used. The first is the White Patch algorithm where the mean of the RGB channel values of the white patch is used to divide the corresponding RGB channel of the incorrectly-white balanced object. For the Gray Patch method we need not use the white patch instead we use the mean of the RGB channels of the image itself and use this to divide the RGB channel of the image.

Presented above are the results of the White Patch (left) and Gray World (right) method. Both for the two sample images, by the way image is wrongly white balanced because the illumination is flourescent light while the white balancing setting of the camera is sunny, the White Patch algorithm produces a better white balancing estimate than the Gray World algorithm. For the White patch recon, one can observe good rendering of colors. Green appears green and so with other colors. In the Gray World recon, there is an observed saturation of colors with white light. The brightness in the latter method is greater than the other.

These images are taken at the same lighting condition as above but the white balancing setting is incandescent with corresponding white patch taken.

Similar to the result for the sunny white balancing setting, White Patch recon is visually better than Gray World. It is observed however that in the White Patch recons, there is an increased saturation of the color. The maroon table for example becomes a striking color maroon and the red hue becomes intense red especially to the image taken at high shutter speed.

The images above are taken at incandescent white balancing setting and the illumination is still flourescent lamp. Again, these two images differ in the NA and shutter speed settings. Objects of interest are the different shades of blue as represented by those different objects. What we want is to obtain correct white balancing using the two methods.

Left images correspond to White Patch rendering algorithm while right images for Gray World. It can be clearly seen that shades of blue color are better rendered for White rather than for Gray. This is due to the incorrect blue shade rendering for Gray. This is because the Gray World algorithm uses the mean of the RGB which in turns averages the whole channel. White patch algo refers to the white patch itself to properly white balance an image. This provides better scaling value because it is based on the mean of the white patch. White Patch method then assures that white will look white when white balanced.

I give myself 9 points for finishing and understanding the activity since I got images of saturated intensities.