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Asked: 2026-06-07T02:40:45+00:00

I am trying to add noise to a BufferedImage in Java, but I am

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I am trying to add noise to a BufferedImage in Java, but I am more interested in the algorithm used to add noise to an image rather than the Java or any other language-specific implementation.

I have searched the web and found out about the Gaussian Noise, but the tutorials/articles either show only code samples that is not very useful to me, or complex mathematical explanations.

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  1. Editorial Team

    It’s not clear what your question is, but here’s some random observations in case they help:

    • If the image is relatively unprocessed (it hasn’t been scaled in size) then the noise in each pixel is roughly independent. So you can simulate that by looping over each pixel in turn, calculating a new noise value, and adding it.

    • Even when images have been processed the approach above is often a reasonable approximation.

    • The amount of noise in an image depends on a lot of factors. For typical images generated by digital sensors a common approximation is that the noise in each pixel is about the same. In other words you choose some standard deviation (SD) and then, in the loop above, select a value from a Gaussian distribution with that SD.

    • For astronomical images (and other low-noise electronic images), there is a component of the noise where the SD is proportional to the square root of the brightness of the pixel.

    So likely what you want to do is:

    • Pick a SD (how noisy you want the image to be)

    • In a loop, for each pixel:

      • Generate a random number from a Gaussian with the given SD (and mean of zero) and add it to the pixel (assuming a greyscale image). For a colour image generate three values and add them to red, green and blue, respectively.

    Update I imagine nightvision is going to be something like astronomical imaging. In which case you might try varying the SD for each pixel so that it includes a constant plus something that depends on the square root of the brightness. So, say, if a pixel has brightness b then you might use 100 + 10 * sqrt(b) as the SD. You’ll need to play with the values but that might look more realistic.

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