albumentations.augmentations.pixel.noise

Random noise to channels: uniform, gaussian, laplace, or beta. spatial_mode: constant, per_pixel, or shared. Params depend on noise_type. This transform generates noise using different probability distributions and applies it to image channels. The noise can be generated in three spatial modes and supports multiple noise distributions, each with configurable parameters.

Parameters

Examples

>>> # Constant RGB shift with different ranges per channel:
>>> transform = AdditiveNoise(
...     noise_type="uniform",
...     spatial_mode="constant",
...     noise_params={"ranges": [(-0.2, 0.2), (-0.1, 0.1), (-0.1, 0.1)]}
... )

Analog film grain: luminance-dependent, spatially correlated noise. Distinct from i.i.d. GaussNoise or ShotNoise. Use for vintage or film-like augmentation. Unlike GaussNoise or ShotNoise, film grain is: - Luminance-dependent: darker areas show more visible grain - Spatially correlated: grain is clumped, not i.i.d. per-pixel - Optionally chromatic: separate grain patterns per channel

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>>
>>> transform = A.FilmGrain(intensity_range=(0.1, 0.3), grain_size_range=(1, 3), p=1.0)
>>> result = transform(image=image)["image"]

Notes

- Grain is generated at lower resolution and upscaled → spatial correlation (clumping) like real film. - Visibility modulated by inverse luminance; darker regions show more grain (silver halide-like behavior).

Add Gaussian (normal) noise to the image. i.i.d. per pixel (or per block if scaled). Use for robustness to sensor or transmission noise. Noise standard deviation and mean are sampled from configurable ranges and scaled to image dtype (255 for uint8, 1.0 for float32). Optional per-channel sampling and lower-resolution noise for speed.

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>>
>>> transform = A.GaussNoise(std_range=(0.1, 0.2), p=1.0)
>>> noisy_image = transform(image=image)["image"]

Notes

- std_range and mean_range are in [0, 1] / [-1, 1]; scaled by 255 (uint8) or used directly (float32). - per_channel=False: faster, same noise on all channels (grayscale-like on RGB). - per_channel=True: different noise per channel (colored noise). - noise_scale_factor < 1 trades speed for noise granularity.

Add camera-sensor-like noise scaling with intensity (high ISO). color_shift and intensity range control strength. Good for low-light or camera noise simulation. This transform adds random noise to an image, mimicking the effect of using high ISO settings in digital photography. It simulates two main components of ISO noise: 1. Color noise: random shifts in color hue 2. Luminance noise: random variations in pixel intensity

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.ISONoise(color_shift=(0.01, 0.05), intensity=(0.1, 0.5), p=0.5)
>>> result = transform(image=image)
>>> noisy_image = result["image"]

Notes

- This transform only works with RGB images. It will raise a TypeError if applied to non-RGB images. - The color shift is applied in the HSV color space, affecting the hue channel. - Luminance noise is added to all channels independently. - This transform can be useful for data augmentation in low-light scenarios or when training models to be robust against noisy inputs.

References

• [{'description': 'ISO noise in digital photography', 'source': 'https://en.wikipedia.org/wiki/Image_noise#In_digital_cameras'}]

Multiply image by random per-pixel or per-channel factor. multiplier_range controls strength. Simulates illumination or gain variation; preserves zeros. This transform multiplies each pixel in the image by a random value or array of values, effectively creating a noise pattern that scales with the image intensity.

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.MultiplicativeNoise(multiplier=(0.9, 1.1), per_channel=True, p=1.0)
>>> result = transform(image=image)
>>> noisy_image = result["image"]

Notes

- When elementwise=False and per_channel=False, a single multiplier is applied to the entire image. - When elementwise=False and per_channel=True, each channel gets a different multiplier. - When elementwise=True and per_channel=False, each pixel gets the same multiplier across all channels. - When elementwise=True and per_channel=True, each pixel in each channel gets a unique multiplier. - Setting per_channel=False is slightly faster, especially for larger images. - This transform can be used to simulate various lighting conditions or to create noise that scales with image intensity.

References

• [{'description': 'Multiplicative noise', 'source': 'https://en.wikipedia.org/wiki/Multiplicative_noise'}]

Apply salt-and-pepper (impulse) noise: randomly set pixels to min or max. amount and salt_vs_pepper control density and ratio. Same mask for all channels. Salt and pepper noise is a form of impulse noise that randomly sets pixels to either maximum value (salt) or minimum value (pepper). The amount and proportion of salt vs pepper can be controlled. The same noise mask is applied to all channels of the image to preserve color consistency.

Parameters

Examples

>>> import albumentations as A
>>> import numpy as np

# Apply salt and pepper noise with default parameters
>>> transform = A.SaltAndPepper(p=1.0)
>>> noisy_image = transform(image=image)["image"]

# Heavy noise with more salt than pepper
>>> transform = A.SaltAndPepper(
...     amount=(0.1, 0.2),       # 10-20% of pixels will be noisy
...     salt_vs_pepper=(0.7, 0.9),  # 70-90% of noise will be salt
...     p=1.0
... )
>>> noisy_image = transform(image=image)["image"]

Notes

- Salt noise sets pixels to maximum value (255 for uint8, 1.0 for float32) - Pepper noise sets pixels to 0 - The noise mask is generated once and applied to all channels to maintain color consistency (i.e., if a pixel is set to salt, all its color channels will be set to maximum value) - The exact number of affected pixels matches the specified amount as masks are generated without overlap

References

• [{'description': 'Digital Image Processing', 'source': 'Rafael C. Gonzalez and Richard E. Woods, 4th Edition, Chapter 5: Image Restoration and Reconstruction.'}, {'description': 'Fundamentals of Digital Image Processing', 'source': 'A. K. Jain, Chapter 7: Image Degradation and Restoration.'}, {'description': 'Salt and pepper noise', 'source': 'https://en.wikipedia.org/wiki/Salt-and-pepper_noise'}]

Shot noise (Poisson) in linear light space. Sensor-realistic; use for low-light or photon-limited imaging and camera simulation. Simulates photon-counting: convert to linear space (gamma removed), treat pixel values as expected photon counts, sample from Poisson, convert back. Variance equals mean in linear space; brighter regions have more absolute noise, less relative.

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>>
>>> transform = A.ShotNoise(scale_range=(0.1, 1.0), p=1.0)
>>> noisy_image = transform(image=image)["image"]

Notes

- Pipeline: linear space (gamma = 2.2), Poisson sample, back to display space. - Preserves mean intensity. Per-pixel, per-channel independent.

References

• [{'description': 'Shot noise', 'source': 'https://en.wikipedia.org/wiki/Shot_noise'}, {'description': 'Original paper', 'source': 'https://doi.org/10.1002/andp.19183622304 (Schottky, 1918)'}, {'description': 'Poisson process', 'source': 'https://en.wikipedia.org/wiki/Poisson_point_process'}, {'description': 'Gamma correction', 'source': 'https://en.wikipedia.org/wiki/Gamma_correction'}]