The output of the program consists of the refined reference images (weighted averages over all experimental images).
The experimental images are not altered at all. In terms of the ML approach, optimal transformations and references for
each image do not play the same role as in the conventional cross-correlation (or least-sqaures) approach. This program
can also be used for reference-free 2D-alignment using only a single reference: just supply --nref 1
.
Although the calculations can be rather time-consuming (especially for many, large experimental images and a large number of references),
we strongly recommend to let the calculations converge. In our experience this takes in the order of 10-100 iterations, depending on the
number images, the amount of noise, etc. The default stopping criterium has yielded satisfactory results in our experience. A parallel
version of this program has been implemented.
See also
mpi_ml_align2d
Parameters
-i <input_file>
--nref <int=1>
--ref <reference_file=>
--oroot <rootname=ml2d>
--mirror
--fast
N_imgs*N_refs
) origin offsets that yield the maximum probability of observing each experimental image, given each of the references. In the first iterations a complete integration over all references, rotations and translations is performed for all images. In all subsequent iterations, for all combinations of experimental images, references and rotations, the probability of observing the image given the optimal origin offsets from the previous iteration is calculated. Then, if this probability is not considered "significant", we assume that none of the other translations will be significant, and we skip the integration over the translations. A combination of experimental image, reference and rotation is considered as "significant" if the probability at the corresponding optimal origin offsets is larger than C times the maximum of all these probabilities for that experimental image and reference (by default C=1e-12) This version may run up to ten times faster than the original, complete-search approach, while practically identical results may be obtained. --thr <N=1>
--iem <blocks=1>
Additional options
--eps <float=5e-5>
--iter <int=100>
--psi_step <float=5.>
--noise <float=1>
--offset <float=3.>
--frac <docfile=>
-C <double=1e-12>
--zero_offsets
--restart <iter=1>
--fix_sigma_noise
--fix_sigma_offset
--fix_fractions
--student <df=6>
--norm
--save_memA
--save_memB
xmipp_ml_align2d -i input/images_some.stk --ref input/seeds2.stk --oroot output/ml2d --fast --mirror