...

MultiplicativeNoise

Targets:
image
volume
Image Types:uint8, float32

Apply multiplicative noise to the input image.

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.

Arguments
multiplier
tuple[float, float]
[0.9,1.1]

The range for the random multiplier. Defines the range from which the multiplier is sampled. Default: (0.9, 1.1)

per_channel
bool
false

If True, use a different random multiplier for each channel. If False, use the same multiplier for all channels. Setting this to False is slightly faster. Default: False

elementwise
bool
false

If True, generates a unique multiplier for each pixel. If False, generates a single multiplier (or one per channel if per_channel=True). Default: False

p
float
0.5

Probability of applying the transform. Default: 0.5

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