tr_gsearch#
- pyrost.bin.tr_gsearch(I_n: numpy.ndarray, W: numpy.ndarray, sigma: numpy.ndarray, I0: numpy.ndarray, u: numpy.ndarray, di: numpy.ndarray, dj: numpy.ndarray, sw_y: float, sw_x: float, grid_size: int, ds_y: float, ds_x: float, loss: str = 'Huber', num_threads: int = 1)#
Update the sample pixel translations by minimizing total mean-squared-error (:math:$MSE_{total}$). Perform a grid search within the search window of sw_y size in pixels for sample translations along the vertical axis, and of sw_x size in pixels for sample translations along the horizontal axis in order to minimize the total MSE.
- Parameters
I_n (numpy.ndarray) – Measured intensity frames.
W (numpy.ndarray) – Measured frames’ white-field.
sigma (numpy.ndarray) – The standard deviation of I_n.
I0 (numpy.ndarray) – Reference image of the sample.
u (numpy.ndarray) – The discrete geometrical mapping of the detector plane to the reference image.
di (numpy.ndarray) – Initial sample’s translations along the vertical detector axis in pixels.
dj (numpy.ndarray) – Initial sample’s translations along the horizontal detector axis in pixels.
sw_y (float) – Search window size in pixels along the vertical detector axis.
sw_x (float) – Search window size in pixels along the horizontal detector axis.
grid_size (int) – Grid size along one of the detector axes. The grid shape is then (grid_size, grid_size).
ds_y (float) – Sampling interval of reference image in pixels along the vertical axis.
ds_x (float) – Sampling interval of reference image in pixels along the horizontal axis.
num_threads (int) – Number of threads.
- Returns
Updated sample pixel translations.
- Return type
Notes
The error metric as a function of sample shifts is given by:
\[\varepsilon_{tr}[n, di^{\prime}, dj^{\prime}] = \frac{1}{Y X} \sum_{i = 0}^Y \sum_{j = 0}^Y f\left( \frac{I[n, i, j] - W[i, j] I_{ref}[u[0, i, j] - di[n] - di^{\prime}, u[1, i, j] - dj[n] - dj^{\prime}]}{\sigma} \right)\]where \(f(x)\) is the Huber loss function.