deeptrack.noises Module#

Features for introducing noise to images.

This module provides classes to add various types of noise to images, including constant offsets, Gaussian noise, and Poisson-distributed noise.

Module Structure#

Classes:

Noise: Abstract base class for noise models.
Background / Offset: Adds a constant value to an image.
Gaussian: Adds IID Gaussian noise.
ComplexGaussian: Adds complex-valued Gaussian noise.
Poisson: Adds Poisson-distributed noise based on signal-to-noise ratio.

Example#

Add Gaussian noise to an image:

>>> import numpy as np
>>> image = np.ones((100, 100))
>>> gaussian_noise = noises.Gaussian(mu=0, sigma=0.1)
>>> noisy_image = gaussian_noise.resolve(image)

Add Poisson noise with a specified signal-to-noise ratio:

>>> poisson_noise = noises.Poisson(snr=0.5)
>>> noisy_image = poisson_noise.resolve(image)

Classes#

`Background`(offset, **kwargs)	Adds a constant value to an image
`ComplexGaussian`([mu, sigma])	Adds complex-valued IID Gaussian noise to an image.
`Feature`([_input])	Base feature class.
`Gaussian`([mu, sigma])	Adds IID Gaussian noise to an image.
`Image`(value[, copy])	Wrapper for array-like values with property tracking.
`Noise`([_input])	Base abstract noise class.
`Offset`	alias of `Background`
`Poisson`(*args[, snr, background, max_val])	Adds Poisson-distributed noise to an image.