I'm actually taking a image processing course right now, and at least one thing I didn't see in this article that I have found very useful is histogram equalization (OpenCV equalizeHist). It basically takes images with low contrast and increases the contrast. This is really useful for many applications but one I've actually been able to use is increasing the legibility of scanned pencil on paper images.
yes! i've found it really useful when dealing with images of items in different lighting conditions. i've struggled with understanding histogram equalization, would you happen to have good resources that explain how it is done?
Does this help any? Say we're looking at a grayscale image, where each pixel has a value between 0.0 and (just less than) 1.0. Sort all the pixel values, then scan them from lowest value to highest. The pixels with the lowest value in the image get reassigned the value 0; and in general, a pixel that's brighter than k of the other pixels, out of n total, gets changed to intensity k/n. This spreads the intensities as evenly as we can. (Which might not be so even, for example if all of the input pixels were the same intensity to start -- then this just changes them to 0!)
Maybe the image is, say, 8-bit grayscale, so the values can range from 0 to 255 instead -- then it'd be floor(k/n times 256).
This is usually described in terms of a cumulative description function; I tried to say the same with less jargon.
