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ISONoise

Targets:
image
volume
Image Types:uint8, float32

Applies camera sensor noise to the input image, simulating high ISO settings.

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
Arguments
color_shift
tuple[float, float]
[0.01,0.05]

Range for changing color hue. Values should be in the range [0, 1], where 1 represents a full 360° hue rotation. Default: (0.01, 0.05)

intensity
tuple[float, float]
[0.1,0.5]

Range for the noise intensity. Higher values increase the strength of both color and luminance noise. Default: (0.1, 0.5)

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.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