As the title suggests , I want to know how noise cancellation is achieved by adding multiple images together . I know the theory behind it , but simple adding the pixel values of 100 noisy images of the same sample would simply produce a white image . Can anybody elaborate , how it is normally done.
This works simply by virtue of the central limit theorem. For a given pixel, you are given 100 observations of its true value + random noise; and if you assume that the noise has zero mean, then the average of these will have a normal distribution centered at the true pixel value, with a variance decreasing as more observations are added. The central limit theorem also tells you that this is a rather terrible denoising method: for a gain in SNR of $6dB$ (reducing noise by a factor of 2) you have to increase the number of averaged samples by a factor of 4.
Regarding your problem, are you sure that you are averaging (dividing by the number of images) rather than just summing? And are you sure that you are using the correct data type for your computations? These are implementation/programming questions and are out of topic here.