def __call__(self, **data_dict):
for b in range(len(data_dict[self.data_key])):
if np.random.uniform() < self.p_per_sample:
data_dict[self.data_key][b] = augment_rician_noise(
data_dict[self.data_key][b],
noise_variance=self.noise_variance)
return data_dict
def rician_noise_generator(generator, noise_variance=(0, 0.1)):
'''
Adds rician noise with the given variance.
The Noise of MRI data tends to have a rician distribution: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2254141/
'''
for data_dict in generator:
assert "data" in list(
data_dict.keys()), "your data generator needs to return a python dictionary with at least a 'data' key value pair"
data_dict["data"] = augment_rician_noise(data_dict['data'], noise_variance=noise_variance)
yield data_dict
def rician_noise_generator(generator, noise_variance=(0, 0.1)):
'''
Adds rician noise with the given variance.
The Noise of MRI data tends to have a rician distribution: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2254141/
'''
for data_dict in generator:
assert "data" in list(
data_dict.keys()
), "your data generator needs to return a python dictionary with at least a 'data' key value pair"
data_dict["data"] = augment_rician_noise(data_dict['data'],
noise_variance=noise_variance)
yield data_dict
def __call__(self, **data_dict):
data_dict["data"] = augment_rician_noise(
data_dict['data'], noise_variance=self.noise_variance)
return data_dict
def __call__(self, **data_dict):
data_dict[self.data_key] = augment_rician_noise(
data_dict[self.data_key], noise_variance=self.noise_variance)
return data_dict