random-noise

random_noise

skimage.util.random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs) [source]

Function to add random noise of various types to a floating-point image.

Parameters:

image : ndarray Input image data. Will be converted to float. mode : str One of the following strings, selecting the type of noise to add: ‘gaussian’ Gaussian-distributed additive noise. ‘localvar’ Gaussian-distributed additive noise, with specified local variance at each point of image ‘poisson’ Poisson-distributed noise generated from the data. ‘salt’ Replaces random pixels with 1. ‘pepper’ Replaces random pixels with 0. ‘s&p’ Replaces random pixels with 0 or 1. ‘speckle’ Multiplicative noise using out = image + n*image, where n is uniform noise with specified mean & variance. seed : int If provided, this will set the random seed before generating noise, for valid pseudo-random comparisons. clip : bool If True (default), the output will be clipped after noise applied for modes ‘speckle’, ‘poisson’, and ‘gaussian’. This is needed to maintain the proper image data range. If False, clipping is not applied, and the output may extend beyond the range [-1, 1]. mean : float Mean of random distribution. Used in ‘gaussian’ and ‘speckle’. Default : 0. var : float Variance of random distribution. Used in ‘gaussian’ and ‘speckle’. Note: variance = (standard deviation) ** 2. Default : 0.01 local_vars : ndarray Array of positive floats, same shape as image, defining the local variance at every image point. Used in ‘localvar’. amount : float Proportion of image pixels to replace with noise on range [0, 1]. Used in ‘salt’, ‘pepper’, and ‘salt & pepper’. Default : 0.05 salt_vs_pepper : float Proportion of salt vs. pepper noise for ‘s&p’ on range [0, 1]. Higher values represent more salt. Default : 0.5 (equal amounts)

Returns:

out : ndarray Output floating-point image data on range [0, 1] or [-1, 1] if the input image was unsigned or signed, respectively.

Notes

Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside the valid image range. The default is to clip (not alias) these values, but they may be preserved by setting clip=False. Note that in this case the output may contain values outside the ranges [0, 1] or [-1, 1]. Use this option with care.

Because of the prevalence of exclusively positive floating-point images in intermediate calculations, it is not possible to intuit if an input is signed based on dtype alone. Instead, negative values are explicity searched for. Only if found does this function assume signed input. Unexpected results only occur in rare, poorly exposes cases (e.g. if all values are above 50 percent gray in a signed image). In this event, manually scaling the input to the positive domain will solve the problem.

The Poisson distribution is only defined for positive integers. To apply this noise type, the number of unique values in the image is found and the next round power of two is used to scale up the floating-point result, after which it is scaled back down to the floating-point image range.

To generate Poisson noise against a signed image, the signed image is temporarily converted to an unsigned image in the floating point domain, Poisson noise is generated, then it is returned to the original range.