ConfocalBlurGenerator3D#

class deepinv.physics.generator.ConfocalBlurGenerator3D(psf_size, num_channels=1, zernike_index=tuple(range(4, 12)), NI=1.51, NA=1.37, lambda_ill=489e-9, lambda_coll=395e-9, pixelsize_XY=50e-9, pixelsize_Z=100e-9, pinhole_radius=1, max_zernike_amplitude=0.1, pupil_size=(512, 512), index_convention='noll', device='cpu', dtype=torch.float32, rng=None, **kwargs)[source]#

Bases: PSFGenerator

Generates the 3D point spread function (PSF) of a confocal laser scanning microsope.

Parameters:

  • psf_size (tuple) – give in the order (depth, height, width)

  • num_channels (int) – number of channels. Default to 1.

  • zernike_index (tuple[int, ...], tuple[tuple[int, int], ...]) –

    activated Zernike coefficients in the following index_convention convention. It can be either:

    • a tuple of int corresponding to the Noll or ANSI indices, in which case the index_convention parameter is required to interpret them correctly.

    • a tuple of tuple[int, int] corresponding to the standard radial-angular indexing \((n,m)\). In this case, the index_convention parameter is ignored.

    Defaults to (4, 5, 6, 7, 8, 9, 10, 11), correspond to radial order n from 2 to 3 (included) and the spherical aberration. These correspond to the following aberrations: defocus, astigmatism, coma, trefoil and spherical aberration.

  • NI (float) – Refractive index of the immersion medium. Defaults to 1.51 (oil),

  • NA (float) – Numerical aperture. Should be less than NI. Defaults to 1.37.

  • lambda_ill (float) – Wavelength of the illumination light (fluorescence excitation). Defaults to 489e-9.

  • lambda_coll (float) – Wavelength of the collection light (fluorescence emission). Defaults to 395e-9.

  • pixelsize_XY (float) – Physical pixel size in the lateral direction (height, width). Defaults to 50e-9.

  • pixelsize_Z (float) – Physical pixel size in the axial direction (depth). Defaults to 100e-9.

  • pinhole_radius (float) – Radius of pinhole in Airy units. Defaults to 1.

  • max_zernike_amplitude (float) – maximum amplitude of Zernike coefficients. Defaults to 0.1.

  • pupil_size (tuple[int]) – pixel size to synthesize the super-resolved pupil. The higher the more precise, defaults to (512, 512). If an int is given, a square pupil is considered.

  • index_convention (str) – convention for the Zernike indices, either 'noll' (default) or 'ansi'.

  • rng (torch.Generator) – random number generator (default to None).

  • device (str) – device (default to cpu).

  • dtype (type) – data type (default to torch.float32).

Examples:

>>> import torch
>>> from deepinv.physics.generator import ConfocalBlurGenerator3D
>>> generator = ConfocalBlurGenerator3D((21, 51, 51), zernike_index=(3,))
>>> print(generator.zernike_polynomials)
['Zernike(n = 1, m = -1) -- Vertical Tilt']
>>> dict = generator.step()
>>> filter = dict['filter']
>>> print(filter.shape)
torch.Size([1, 1, 21, 51, 51])
>>> batch_size = 2
>>> n_zernike = len(generator.generator_ill.generator2d.zernike_index)
>>> dict = generator.step(batch_size=batch_size,
...                       coeff_ill = 0.1 * torch.rand(batch_size, n_zernike),
...                       coeff_coll = 0.1 * torch.rand(batch_size, n_zernike))
>>> dict.keys()
dict_keys(['filter', 'coeff_ill', 'coeff_coll'])
step(batch_size=1, coeff_ill=None, coeff_coll=None, **kwargs)[source]#

Generate a batch of 3D confocal PSF with a batch of Zernike coefficients for illumination and collection

Parameters:

  • batch_size (int) – number of PSFs to generate.

  • coeff_ill (torch.Tensor) – tensor of size batch_size x len(zernike_index) containing the Zernike coefficients for illumination. If None, random coefficients are generated.

  • coeff_coll (torch.Tensor) – tensor of size batch_size x len(zernike_index) containing the Zernike coefficients for collection. If None, random coefficients are generated.

Returns:

dictionary with keys

  • filter: tensor of size batch_size x num_channels x psf_size[0] x psf_size[1] batch of PSFs,

  • coeff_ill: list of sampled Zernike coefficients in this realization of illumination,

  • coeff_coll: list of sampled Zernike coefficients in this realization of collection,

Return type:

dict

property zernike_polynomials: list[str]#

List of Zernike polynomials used in the decomposition, with the corresponding aberration if available.

Examples using ConfocalBlurGenerator3D:#

3D diffraction PSF

3D diffraction PSF