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Use Gaussian noise in place of Poisson noise beyond threshold to improve performance #45

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Use Gaussian noise in place of Poisson noise beyond threshold to improve performance #45

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9 changes: 7 additions & 2 deletions nionswift_plugin/usim/Noise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,16 @@ def __init__(self) -> None:
self.enabled = True
self.poisson_level: typing.Optional[float] = None

def apply(self, input: DataAndMetadata.DataAndMetadata) -> DataAndMetadata.DataAndMetadata:
def apply(self, input: DataAndMetadata.DataAndMetadata, lambda_thresh: float = 1.0) -> DataAndMetadata.DataAndMetadata:
if self.enabled and self.poisson_level:
rs = numpy.random.RandomState() # use this to avoid blocking other calls to poisson
input_data = input.data
assert input_data is not None
poisson_data = typing.cast(_NDArray, rs.poisson(self.poisson_level, size=input_data.shape).astype(input_data.dtype))
if self.poisson_level > lambda_thresh:
# Since it is 'high' lambda, we can approximate it to a normal distribution
poisson_data = typing.cast(_NDArray, rs.normal(loc=self.poisson_level, scale=numpy.sqrt(self.poisson_level), size=input_data.shape).astype(input_data.dtype))
else:
poisson_data = typing.cast(_NDArray, rs.poisson(self.poisson_level, size=input_data.shape).astype(input_data.dtype))
return input + (poisson_data - self.poisson_level)
return input