DiffractionBlurGenerator

class deepinv.physics.generator.DiffractionBlurGenerator(psf_size: tuple, num_channels: int = 1, device: str = 'cpu', dtype: type = torch.float32, rng: Generator | None = None, list_param: List[str] = ['Z4', 'Z5', 'Z6', 'Z7', 'Z8', 'Z9', 'Z10', 'Z11'], fc: float = 0.2, max_zernike_amplitude: float = 0.15, pupil_size: Tuple[int] = (256, 256))

Bases: PSFGenerator

Diffraction limited blur generator.

Generates 2D diffraction kernels in optics using Zernike decomposition of the phase mask (Fresnel/Fraunhoffer diffraction theory).

Zernike polynomials are ordered following the Noll’s sequential indices convention (https://en.wikipedia.org/wiki/Zernike_polynomials for more details).

Parameters:

• psf_size (tuple) – the shape H x W of the generated PSF in 2D

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

• list_param (list[str]) – list of activated Zernike coefficients in Noll’s convention, defaults to ["Z4", "Z5", "Z6","Z7", "Z8", "Z9", "Z10", "Z11"].

• fc (float) – cutoff frequency (NA/emission_wavelength) * pixel_size. Should be in [0, 0.25] to respect the Shannon-Nyquist sampling theorem, defaults to 0.2.

• pupil_size (tuple[int]) – this is used to synthesize the super-resolved pupil. The higher the more precise, defaults to (256, 256). If a single int is given, a square pupil is considered.

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Examples:

>>> from deepinv.physics.generator import DiffractionBlurGenerator
>>> generator = DiffractionBlurGenerator((5, 5), num_channels=3)
>>> blur = generator.step()  # dict_keys(['filter', 'coeff', 'pupil'])
>>> print(blur['filter'].shape)
torch.Size([1, 3, 5, 5])

generate_coeff(batch_size)

Generates random coefficients of the decomposition in the Zernike polynomials.

Parameters:

batch_size (int) – batch_size.

Returns:

a tensor of shape (batch_size, len(list_param)) coefficients in the Zernike decomposition.

step(batch_size: int = 1, coeff: Tensor | None = None, seed: int | None = None)

Generate a batch of PFS with a batch of Zernike coefficients

Parameters:

• batch_size (int) – batch_size.

• coeff (torch.Tensor) – batch_size x len(list_param) coefficients of the Zernike decomposition (defaults is None)

• seed (int) – the seed for the random number generator.

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’: list of sampled Zernike coefficients in this realization, ‘pupil’: the pupil function

Return type:

dict

Examples using DiffractionBlurGenerator:

A tour of blur operators

A tour of blur operators