Imaging artifacts

Note

To ensure accuracy and avoid introducing ailiasing artifacts, the input data should be at least Nyquist sampled when applying imaging artifacts via convolution.

Smear

Smear is used to represent image motion with a relatively low temporal frequency relative to integration time. The motion occurs in a slowly varying or fixed direction over one integration time. Lentil’s smear() method represents smear as a linear directional blur over some distance (or number of pixels):

>>> import lentil
>>> import matplotlib.pyplot as plt
>>> psf = ...  # PSF calculation details omitted
>>> psf_smear = lentil.smear(psf, distance=5e-5,
...                          pixelscale=5e-6,
...                          oversample=5)
>>> plt.subplot(121), plt.imshow(psf, cmap='inferno')
>>> plt.subplot(122), plt.imshow(psf_smear, cmap='inferno')

As an alternative to specifying physical distance and pixelscale, a number of pixels can also be provided:

>>> import lentil
>>> import matplotlib.pyplot as plt
>>> psf = ...  # PSF calculation details omitted
>>> psf_smear = lentil.smear(psf, distance=10,
...                          oversample=5)
>>> plt.subplot(121), plt.imshow(psf, cmap='inferno')
>>> plt.subplot(122), plt.imshow(psf_smear, cmap='inferno')

The default behavior is to choose a new random smear direction each time smear() is called, but a static direction can optionally be specified as needed:

>>> import lentil
>>> import matplotlib.pyplot as plt
>>> psf = ...  # PSF calculation details omitted
>>> psf_smear = lentil.smear(psf, distance=25,
...                          angle=30)
>>> plt.subplot(121), plt.imshow(psf, cmap='inferno')
>>> plt.subplot(122), plt.imshow(psf_smear, cmap='inferno')

Jitter

Jitter is used to represent image motion with a relatively high temporal frequency relative to integration time. Lentil’s jitter() method represents jitter with a Gaussian blur operation. Note this approach is only valid if the motion occurs randomly in all directions during one integration time.

>>> import lentil
>>> import matplotlib.pyplot as plt
>>> psf = ...  # PSF calculation details omitted
>>> psf_jitter = lentil.jitter(psf, scale=2, oversample=5)
>>> plt.subplot(121), plt.imshow(psf, cmap='inferno')
>>> plt.subplot(122), plt.imshow(psf_jitter, cmap='inferno')

If the jitter being modeled is not sufficiently random during a typical integration time, a timeseries should be used instead. This can have a major impact on propagation performance but will provide the most accurate results.