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Showing content from https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_gaussian_blur below:

RandomGaussianBlur layer

[source]

keras.layers.RandomGaussianBlur(
    factor=1.0,
    kernel_size=3,
    sigma=1.0,
    value_range=(0, 255),
    data_format=None,
    seed=None,
    **kwargs
)

Applies random Gaussian blur to images for data augmentation.

This layer performs a Gaussian blur operation on input images with a randomly selected degree of blurring, controlled by the factor and sigma arguments.

Arguments

• factor: A single float or a tuple of two floats. factor controls the extent to which the image hue is impacted. factor=0.0 makes this layer perform a no-op operation, while a value of 1.0 performs the most aggressive blurring available. If a tuple is used, a factor is sampled between the two values for every image augmented. If a single float is used, a value between 0.0 and the passed float is sampled. Default is 1.0.
• kernel_size: Integer. Size of the Gaussian kernel used for blurring. Must be an odd integer. Default is 3.
• sigma: Float or tuple of two floats. Standard deviation of the Gaussian kernel. Controls the intensity of the blur. If a tuple is provided, a value is sampled between the two for each image. Default is 1.0.
• value_range: the range of values the incoming images will have. Represented as a two-number tuple written [low, high]. This is typically either [0, 1] or [0, 255] depending on how your preprocessing pipeline is set up.
• seed: Integer. Used to create a random seed.

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