etomo.operators.linear.HOTV
etomo.operators.linear.HOTV#
TV and HOTV class
- class HOTV(img_shape, order=1, **kwargs)[source]#
Bases:
etomo.operators.linear.base.LinearBase
The HOTV computation class for 2D image decomposition
Note
At the moment, assumed that the image is square
- _op(data)[source]#
Define the HOTV operator. This method returns the input data convolved with the HOTV filter.
- Parameters
data (numpy.ndarray((m', m'))) – input 2D data array.
- Returns
coeffs – the variation values.
- Return type
numpy.ndarray((2 * m’ * m’))
- _adj_op(coeffs)[source]#
Define the HOTV adjoint operator. This method returns the adjoint of HOTV computed image.
- Parameters
coeffs (numpy.ndarray((2 * m' * m'))) – the HOTV coefficients.
- Returns
data – the reconstructed data.
- Return type
numpy.ndarray((m’, m’))
- class HOTV_3D(img_shape, nb_slices, order=1, **kwargs)[source]#
Bases:
etomo.operators.linear.base.LinearBase
The HOTV computation class for 3D image decomposition
Note
At the moment, assumed that the image is square in x-y directions
- _op(data)[source]#
Define the HOTV operator. This method returns the input data convolved with the HOTV filter.
- Parameters
data (numpy.ndarray((p', m', m'))) – input 3D data array.
- Returns
coeffs – the variation values.
- Return type
numpy.ndarray((3 * p’ * m’ * m’))
- _adj_op(coeffs)[source]#
Define the HOTV adjoint operator. This method returns the adjoint of HOTV computed image.
- Parameters
coeffs (numpy.ndarray((3 * p' * m' * m'))) – the HOTV coefficients.
- Returns
data – the reconstructed data.
- Return type
numpy.ndarray((p’, m’, m’))