Inline Holography Using Lower-Level Functions

As an alternative to using the Holo Class as described in Inline Holography Using the Holo Class, low-level functions can be called directly. For most applications this is not the recommended approach as it involves more steps and the API is more likely to change in the future, however it may be necessary if implementing more customised processing pipelines.

Begin by importing the library:

import pyholoscope as pyh

Before we can refocus we define a propagator using pyholoscope.propagator(). This requires specification of the hologram size, wavelength, pixel size and the depth we wish to refocus to, e.g.:

grid_size = 1024
wavelength = 0.5e-6    # metres
pixel_size = 2.0e-6    # metres
depth = 1.0e-3         # metres
prop = pyh.propagator(grid_size, wavelength, pixel_size, depth)

The propagation model can be changed by passing propagation_method = "fresnel" (default is "angular_spectrum"):

prop = pyh.propagator(grid_size, wavelength, pixel_size, depth, propagation_method = "fresnel")

When using a point source, effective-magnification correction can be enabled by passing correct_pixel_size = True and source_distance (source-to-camera distance, in the same units as pixel_size):

prop = pyh.propagator(grid_size, wavelength, pixel_size, depth,
                      correct_pixel_size = True, source_distance = 0.01)

Assuming we have an inline hologram as a 2D numpy array hologram we can then refocus using pyholoscope.refocus():

refocused_img = pyh.refocus(hologram, prop, background = background_img)

Here we have also provided an optional background hologram, background_img, again a 2D numpy array. The returned image is a 2D complex numpy array, to obtain the amplitude image as 2D numpy array use:

refocused_amp = pyh.amplitude(refocused_img)

Flat-fielding (normalisation) and windowing can also be applied by passing 2D numpy arrays using normalise= and window= respectively. Windows can be generated manually or by using the pyholoscope.circ_window(), pyholoscope.circ_cosine_window() or pyholoscope.square_cosine_window() functions. For example, to create a square cosine window which drops to 0 at the edges of the image, with a skin thickness of 10 pixels we could do the following:

imgSize = np.shape(hologram)
radius = (np.shape(hologram)[1] / 2, np.shape(hologram)[0] / 2)
skin_thickness = 10
window = pyh.square_cosine_window(imgSize, radius, skin_thickness)

and then call:

refocused_img = pyh.refocus(hologram, prop, background = background_img, window = window, normalise = background_img)