The sample complexity of sparse multi-reference alignment and single-particle cryo-electron microscopy

TL;DR

This paper investigates the sample complexity of sparse multi-reference alignment and cryo-EM, revealing conditions under which signals can be recovered from second moments with near-optimal sample complexity bounds.

Contribution

It analyzes the second moment in MRA and cryo-EM, establishing sparsity conditions for signal recovery and deriving near-optimal sample complexity bounds using representation theory.

Findings

Second moment determines signals up to unitary transformations.

Sparsity enables recovery with sample complexity n=ω(σ^4).

Cryo-EM can recover signals with at most one-third non-zero coefficients, near the theoretical limit.

Abstract

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 $n=\omega(\sigma^{2d})$, where $n$ is the number of observations, $\sigma^2$ is the variance of the noise, and $d$ is the lowest-order moment of the observations that uniquely determines the signal. In particular, it was shown that in many cases, $d=3$ for generic signals, and thus the sample complexity is $n=\omega(\sigma^6)$. 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 $n=\omega(\sigma^4)$. Notably, we show that the sample complexity of cryo-EM is $n=\omega(\sigma^4)$ 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.