Double-precision images are used when the number of photons per pixel can be much larger than 65535 (but less than 10^12) the intensity values vary between 0 and 1 and correspond to the number of photons divided by 10^12.Īdds salt and pepper noise to the image I, where d is the noise density.
In order to respect Poisson statistics, the intensities of unit8 and uint16 images must correspond to the number of photons (or any other quanta of information). Generates Poisson noise from the data instead of adding artificial noise to the data. The image_intensity vector must contain normalized intensity values ranging from 0 to 1. The image_intensity and var arguments are vectors of the same size, and plot(image_intensity,var) plots the functional relationship between noise variance and image intensity.
J = imnoise(I,'localvar',image_intensity,var)Īdds zero-mean, Gaussian noise to an image I, where the local variance of the noise, var, is a function of the image intensity values in I. The default is zero mean noise with 0.01 variance.Īdds zero-mean, Gaussian white noise of local variance V to the image I. All numerical parameters are normalized they correspond to operations with images with intensities ranging from 0 to 1.Īdds Gaussian white noise of mean m and variance v to the image I. If you omit these arguments, imnoise uses default values for the parameters. Zero-mean Gaussian white noise with an intensity-dependent varianceĭepending on which type of noise you specify, imnoise accepts additional parameters. Gaussian white noise with constant mean and variance type is a string that can have one of these values. J = imnoise(I,type) adds noise of a given type to the intensity image I. Imnoise (Image Processing Toolbox User's Guide) Image Processing Toolbox User's Guide
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