I am writing an application that averages / combines / groups a number of exposures. This is commonly used to reduce noise in the resulting image.
However, it seems that to optimize the average / stack, exposures are usually normalized first. It seems that this process assigns weights to each of the expositions, and then proceeds to combine them. I assume that the process calculates the total intensity of each image, since the goal is to match the intensity of all the images in the stack.
My question is, how can I turn on an algorithm that will allow me to normalize a series of images? I suppose the question will be generalized, instead it will ask: "How can I normalize a series of readings?"
The outline in my head looks like this:
- Calculate the average value of the reference image.
- Separate the average value of each frame from the average value of the reference frame.
- The result of each division is the weight for each frame.
- Scale / Multiply each pixel in a frame by the weight found for that particular frame.
Does this seem clear to anyone? I tried Google in the last hour, but found nothing. They also took indexes of various books on image processing on Amazon, but nothing came of it.
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