ipfml.filters¶

ipfml.filters.noise¶

ipfml.filters.noise.cauchy_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.0002)¶

Cauchy noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.0002)

Returns:

2D Numpy array with Cauchy noise applied

Example:

>>> from ipfml.filters.noise import cauchy_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = cauchy_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.gaussian_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1)¶

Gaussian noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)

Returns:

2D Numpy array with gaussian noise applied

Example:

>>> from ipfml.filters.noise import gaussian_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = gaussian_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.laplace_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1)¶

Laplace noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)

Returns:

2D Numpay array with Laplace noise applied

Example:

>>> from ipfml.filters.noise import laplace_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = laplace_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.log_normal_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.05)¶

Log-normal noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.05)

Returns:

2D Numpy array with Log-normal noise applied

Example:

>>> from ipfml.filters.noise import log_normal_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = log_normal_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.mut_white_noise(image, n, identical=False, distribution_interval=(-0.5, 0.5), k=0.2)¶

Multiplied White noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (-0.5, 0.5))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)

Returns:

2D Numpy array with multiplied white noise applied

Example:

>>> from ipfml.filters.noise import mut_white_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = mut_white_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.white_noise(image, n, identical=False, distribution_interval=(-0.5, 0.5), k=0.2)¶

White noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (-0.5, 0.5))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)

Returns:

2D Numpy array with white noise applied

Example:

>>> from ipfml.filters.noise import white_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = white_noise(image, 10)
>>> noisy_image.shape
(100, 100)