747 research outputs found
Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms
The number of noisy images required for molecular reconstruction in
single-particle cryo-electron microscopy (cryo-EM) is governed by the
autocorrelations of the observed, randomly-oriented, noisy projection images.
In this work, we consider the effect of imposing sparsity priors on the
molecule. We use techniques from signal processing, optimization, and applied
algebraic geometry to obtain new theoretical and computational contributions
for this challenging non-linear inverse problem with sparsity constraints. We
prove that molecular structures modeled as sums of Gaussians are uniquely
determined by the second-order autocorrelation of their projection images,
implying that the sample complexity is proportional to the square of the
variance of the noise. This theory improves upon the non-sparse case, where the
third-order autocorrelation is required for uniformly-oriented particle images
and the sample complexity scales with the cube of the noise variance.
Furthermore, we build a computational framework to reconstruct molecular
structures which are sparse in the wavelet basis. This method combines the
sparse representation for the molecule with projection-based techniques used
for phase retrieval in X-ray crystallography.Comment: 31 pages, 5 figures, 1 movi
Manifold Rewiring for Unlabeled Imaging
Geometric data analysis relies on graphs that are either given as input or
inferred from data. These graphs are often treated as "correct" when solving
downstream tasks such as graph signal denoising. But real-world graphs are
known to contain missing and spurious links. Similarly, graphs inferred from
noisy data will be perturbed. We thus define and study the problem of graph
denoising, as opposed to graph signal denoising, and propose an approach based
on link-prediction graph neural networks. We focus in particular on
neighborhood graphs over point clouds sampled from low-dimensional manifolds,
such as those arising in imaging inverse problems and exploratory data
analysis. We illustrate our graph denoising framework on regular synthetic
graphs and then apply it to single-particle cryo-EM where the measurements are
corrupted by very high levels of noise. Due to this degradation, the initial
graph is contaminated by noise, leading to missing or spurious edges. We show
that our proposed graph denoising algorithm improves the state-of-the-art
performance of multi-frequency vector diffusion maps
3D unknown view tomography via rotation invariants
In this paper, we study the problem of reconstructing a 3D point source model
from a set of 2D projections at unknown view angles. Our method obviates the
need to recover the projection angles by extracting a set of rotation-invariant
features from the noisy projection data. From the features, we reconstruct the
density map through a constrained nonconvex optimization. We show that the
features have geometric interpretations in the form of radial and pairwise
distances of the model. We further perform an ablation study to examine the
effect of various parameters on the quality of the estimated features from the
projection data. Our results showcase the potential of the proposed method in
reconstructing point source models in various noise regimes
The sample complexity of sparse multi-reference alignment and single-particle cryo-electron microscopy
Multi-reference alignment (MRA) is the problem of recovering a signal from
its multiple noisy copies, each acted upon by a random group element. MRA is
mainly motivated by single-particle cryo-electron microscopy (cryo-EM) that has
recently joined X-ray crystallography as one of the two leading technologies to
reconstruct biological molecular structures. Previous papers have shown that in
the high noise regime, the sample complexity of MRA and cryo-EM is
, where is the number of observations, is
the variance of the noise, and is the lowest-order moment of the
observations that uniquely determines the signal. In particular, it was shown
that in many cases, for generic signals, and thus the sample complexity
is .
In this paper, we analyze the second moment of the MRA and cryo-EM models.
First, we show that in both models the second moment determines the signal up
to a set of unitary matrices, whose dimension is governed by the decomposition
of the space of signals into irreducible representations of the group. Second,
we derive sparsity conditions under which a signal can be recovered from the
second moment, implying sample complexity of . Notably, we
show that the sample complexity of cryo-EM is if at most
one third of the coefficients representing the molecular structure are
non-zero; this bound is near-optimal. The analysis is based on tools from
representation theory and algebraic geometry. We also derive bounds on
recovering a sparse signal from its power spectrum, which is the main
computational problem of X-ray crystallography
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