aug_hetero_noise — Signal-dependent heteroscedastic noise¶
Group: Augmentation · C ABI: n4m_augmentation_hetero_noise_*
Description¶
Noise whose standard deviation depends on signal magnitude.
Parameters¶
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API and bindings¶
C ABI (ABI 2): n4m_augmentation_hetero_noise_apply · n4m_augmentation_hetero_noise_create · n4m_augmentation_hetero_noise_destroy. Use the linked public header for the exact signature, configuration, and result handles.
Python (verified public re-export):
from n4m.augmentation.noise import HeteroscedasticNoiseAugmenter
Source signature: HeteroscedasticNoiseAugmenter(noise_base: float = 0.001, noise_signal_dep: float = 0.01, rng: Optional[PCG64] = None, seed: int = 0).
R: no current source-verified entry point was found for this catalog method.
MATLAB / Octave: no current source-verified entry point was found for this catalog method.
Explanations¶
Bibliographic source¶
No single publication defines this affine noise law. It is an internal measurement-noise heuristic documented by the native implementation.
Mathematical principle¶
At cell \((i,j)\) the noise scale is \(s_{ij}=b+c|X_{ij}|\), where \(b\) is noise_base and \(c\) is noise_signal_dep; the output is \(X'_{ij}=X_{ij}+s_{ij}Z_{ij}\) with \(Z_{ij}\sim\mathcal N(0,1)\).
Appropriate uses¶
Simulating instruments whose absolute noise increases with signal magnitude.
Limits and validation¶
The affine variance law is phenomenological, independent across channels, and can yield a negative scale if parameters are chosen outside their intended non-negative range. It is not a calibrated photon-counting model.
Implementation¶
Python role API n4m.augmentation.noise.HeteroscedasticNoiseAugmenter; ABI 2 family n4m_augmentation_hetero_noise_{create,apply,destroy}.
The ABI-2 implementation is the n4m_augmentation_hetero_noise_* lifecycle in libn4m.
Sources and provenance¶
See also: methods index · interactive dashboard