def reweight(self, x_new):
""" Updat the weights.
Parameters
----------
x_new: ndarray
the current primal solution.
Returns
-------
sigma_est: ndarray
the variance estimate on each scale.
"""
self.linear_op.op(x_new)
weights = np.empty((0, ), dtype=self.weights.dtype)
sigma_est = []
for scale in range(self.linear_op.transform.nb_scale):
bands_array, _ = flatten(self.linear_op.transform[scale])
if scale == (self.linear_op.transform.nb_scale - 1):
std_at_scale_i = 0.
else:
std_at_scale_i = sigma_mad(bands_array)
sigma_est.append(std_at_scale_i)
thr = np.ones(bands_array.shape, dtype=weights.dtype)
thr *= self.thresh_factor * std_at_scale_i
weights = np.concatenate((weights, thr))
self.weights = weights
return sigma_est
def _op(self, data):
if isinstance(data, np.ndarray):
data = pysap.Image(data=data)
self.transform.data = data
self.transform.analysis()
coeffs, coeffs_shape = flatten(self.transform.analysis_data)
return coeffs, coeffs_shape
def _op(self, data):
if isinstance(data, np.ndarray):
data = pysap.Image(data=data)
# Get the transform from queue
transform = self.transform_queue.pop()
transform.data = data
transform.analysis()
coeffs, coeffs_shape = flatten(transform.analysis_data)
# Add back the transform to the queue
self.transform_queue.append(transform)
return coeffs, coeffs_shape
def _op(self, data):
""" Define the wavelet operator for single channel.
This is internal function that returns wavelet coefficients for a
single channel
Parameters
----------
data: ndarray or Image
input 2D data array.
Returns
-------
coeffs: ndarray
the wavelet coefficients.
"""
coefs_real = filter_convolve(data.real, self.transform)
coefs_imag = filter_convolve(data.imag, self.transform)
coeffs, coeffs_shape = flatten(coefs_real + 1j * coefs_imag)
return coeffs, coeffs_shape
def op(self, data):
""" Define the wavelet operator.
This method returns the input data convolved with the wavelet filter.
Parameters
----------
data: ndarray or Image
input 2D data array.
Returns
-------
coeffs: ndarray
the wavelet coefficients.
"""
if isinstance(data, numpy.ndarray):
data = pysap.Image(data=data)
self.transform.data = data
self.transform.analysis()
coeffs, self.coeffs_shape = flatten(self.transform.analysis_data)
return coeffs