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Image noise can be generated by adding random variations to pixel values in an image. This can be achieved using various noise models, such as Gaussian noise, which adds values sampled from a Gaussian distribution to each pixel, or salt-and-pepper noise, which randomly replaces some pixels with maximum or minimum values. Software tools and libraries like Python’s NumPy or OpenCV can be used to implement these techniques programmatically. Adjusting the parameters of the noise model allows for control over the noise intensity and characteristics.

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