ipfml.filters.noise

Noise filters to apply on images

Functions

cauchy_noise(image, n[, identical, …]) Cauchy noise filter to apply on image
gaussian_noise(image, n[, identical, …]) Gaussian noise filter to apply on image
laplace_noise(image, n[, identical, …]) Laplace noise filter to apply on image
log_normal_noise(image, n[, identical, …]) Log-normal noise filter to apply on image
mut_white_noise(image, n[, identical, …]) Multiplied White noise filter to apply on image
salt_pepper_noise(image, n[, identical, p, k]) Pepper salt noise filter to apply on image
white_noise(image, n[, identical, …]) White noise filter to apply on image
ipfml.filters.noise.cauchy_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.0002)[source]

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)[source]

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)[source]

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)[source]

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, 1), k=0.002)[source]

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, 1))
  • k – variable that specifies the amount of noise to be taken into account in the output image (default 0.002)
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.salt_pepper_noise(image, n, identical=False, p=0.1, k=0.5)[source]

Pepper salt 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)
  • p – probability to increase pixel value otherwise decrease it
  • k – variable that specifies the amount of noise to be taken into account in the output image (default 0.5)
Returns:

2D Numpy array with salt and pepper noise applied

Example:

>>> from ipfml.filters.noise import salt_pepper_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = salt_pepper_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)[source]

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)