Aligner class for phase correlation (PC) strategy.

If use_cuda is False, shifts are determined using PC as implemented in scikit-image. Based on: Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup. Efficient subpixel image registration algorithms, Optics Letters vol. 33 (2008) pp. 156-158. https://doi.org/10.1364/OL.33.000156

If use_cuda is True, shifts are determined using a custom implementation of the same PC algorithm which is optimized for GPU-acceleration using CuPy and CUDA.

Initialiazer for phase correlation alignment strategy.

Atrributes¶

startpy:class:~int

Position in tilt series to use as starting point for the alignment

upsample_factorpy:class:~int

Factor by which to resample the data for phase correlation

use_cudapy:class:~bool

Enable/disable the use of GPU-accelerated processes using CUDA

show_progressbarpy:class:~bool

Enable/disable progress bar

Constructors¶

PhaseCorrelationAligner(stack: TomoStack, ...)[source]

Initialize self. See help(type(self)) for accurate signature.

align¶

calculate_shifts() → ndarray[source]

Calculate shifts using the phase correlation algorithm.

Methods¶

align(shift_method: 'interp' | 'fourier' = 'fourier') → TomoStack[source]

Perform stack alignment.