Phase retrieval in infinite-dimensional Hilbert spaces
By Jameson Cahill, Peter G. Casazza, and Ingrid Daubechies
Abstract
The main result of this paper states that phase retrieval in infinite-dimensional Hilbert spaces is never uniformly stable, in sharp contrast to the finite-dimensional setting in which phase retrieval is always stable. This leads us to derive stability results for signals depending on how well they are approximated by finite expansions.
1. Introduction
Given a separable Hilbert space $\mathcal{H}$, phase retrieval deals with the problem of recovering an unknown $f\in \mathcal{H}$ from a set of intensity measurements $(|\langle f,\varphi _n\rangle |)_{n\in I}$ for some countable collection $\Phi =\{\varphi _n\}_{n\in I}\subseteq \mathcal{H}$. Note that if $f=\alpha g$ with $|\alpha |=1$, then $|\langle f,\varphi _n\rangle |=|\langle g,\varphi _n\rangle |$ for every $n\in I$ regardless of our choice of $\Phi$; we say $\Phi$does phase retrieval if the converse of this statement is true, i.e., if the equalities $|\langle f,\varphi _n\rangle |=|\langle g,\varphi _n\rangle |$ for every $n$ imply that there is a unimodular scalar $\alpha$ so that $f=\alpha g$.
We will generally assume that $\Phi$ forms a frame for $\mathcal{H}$, i.e., there are positive constants $0<A\leq B<\infty$ so that
so that $\Phi$ does phase retrieval if and only if $\mathcal{A}_{\Phi }$ is injective on $\mathcal{H}/\sim$, where $f\sim g$ if $f=\alpha g$ with $|\alpha |=1$.
This theorem was originally proved in Reference 3 where it was only stated in the finite-dimensional case, but the proof still holds in infinite dimensions without any modifications. The original proof of part (a) presented in Reference 3 did not give the correct conclusion in the case where $\mathcal{H}$ is a Hilbert space over the complex numbers. This was observed by the authors of Reference 5 where they presented a much more complicated proof for this case. It turns out that the proof presented in Reference 3 does hold in this case with only minor modifications, which is the proof presented above.
We remark here that recently several papers have been devoted to showing that certain frames do phase retrieval for infinite-dimensional spaces over both the real and complex numbers, so by Theorem 1.2 all of these frames have the complement property. For instance, in Reference 10 it is shown that a real-valued band-limited signal can be recovered up to sign from the absolute values of its samples at any rate greater than twice the Nyquist rate. A similar result for complex-valued band-limited signals was shown in Reference 9 which required a minimal oversampling rate of four times the Nyquist rate.
In Reference 8 the authors study an instance of the phase retrieval problem using the Cauchy wavelet transform to recover analytic functions in $L^2(\mathbb{R},\mathbb{C})$ that have compactly supported Fourier transforms. In that paper they observe that although they are able to show that $\mathcal{A}_{\Phi }$ is injective (for the particular choice of $\mathcal{H}$ and $\Phi$) there is an inherent lack of robustness in the sense that arbitrarily small perturbations of the measurements $\mathcal{A}_{\Phi }(f)$ can result in large errors in the reconstructed signal (see sections 4.1 and 4.2 in Reference 8). The main result of the present paper states that this type of lack of robustness is unavoidable when doing phase retrieval in an infinite-dimensional Hilbert space.
In this paper, we restrict ourselves to the case of countably infinite frames in Hilbert spaces; in work extending the present results, Reference 1 proves a similar lack of robustness for phase retrieval in infinite-dimensional Banach spaces with infinite frames that need not be countable.
One way to quantify the robustness of the phase retrieval process for a given frame $\Phi$ is in terms of the lower Lipschitz bound of the map $\mathcal{A}_{\Phi }$ with respect to some metric on the space $\mathcal{H}/\sim$. A natural choice of metric is the quotient metric induced by the metric on $\mathcal{H}$ given by
In Reference 5 the authors introduced a numerical version of the complement property as a means of quantifying the constant $C$ in Equation 1.1:
In Reference 5 it is shown that when $\mathcal{H}=\mathbb{R}^M$ the lower Lipschitz bound of $\mathcal{A}_{\Phi }$ is precisely controlled by the largest $\sigma$ for which $\Phi$ has the $\sigma$-strong complement property (see also Reference 4). Although this result does not apply to the complex case, much like the complement property cannot be used to determine whether a given frame does phase retrieval for a complex space, we still have the following result in the finite-dimensional case.
2. Main results
Before stating the main result we first remark that, when viewed as a subset of $\mathbb{C}^{M\times N}$, the set of frames $\{\varphi _n\}_{n=1}^N$ that do phase retrieval for $\mathbb{C}^M$ is an open subset for each $N$; see Reference 2Reference 7. In fact, in Reference 7 it is shown that if $N\geq 4M-4$, then this set is open and dense, and it is clear that it must be empty if $N$ is sufficiently small. At this time it is not known if there exists a pair $(M,N)$ where the set of frames consisting of $N$ vectors which do phase retrieval for $\mathbb{C}^M$ is nonempty but not dense (see Reference 11), but in any case, the set of frames which do not do phase retrieval is never dense unless it is all of $\mathbb{C}^{M\times N}$. The next statement says that this situation is reversed when we consider frames for an infinite-dimensional space.
The above proposition suggests an infinite-dimensional space is fundamentally different from a finite-dimensional setting when doing phase retrieval. In particular, since any frame can be perturbed by an arbitrarily small amount to arrive at a frame that does not do phase retrieval, it suggests that phase retrieval for infinite-dimensional spaces is inherently unstable. We now state the main result, confirming this intuition.
Before proving the theorem we need a lemma.
Note that one consequence of the above lemma is that no frame for an infinite-dimensional Hilbert space can have the $\sigma$-strong complement property, regardless of how small one picks $\sigma >0$.
Since Theorem 2.2 says that we can never do phase retrieval in a robust way for an infinite-dimensional space, but Proposition 1.4 says we can basically always do it for a finite-dimensional space, it seems natural to try to use finite-dimensional approximations when working in an infinite-dimensional setting. Our next theorem is a first step in that direction; again, we first establish a lemma.
This theorem complements Theorem 2.2: even though uniformly stable phase retrieval is never possible in infinite-dimensional $\mathcal{H}$, Theorem 2.7 establishes that stable phase retrieval is possible for elements of $\mathcal{H}$ that can be approximated sufficiently well by finite-dimensional expansions, and quantifies the “extent” of this restricted stability.
Note that we did not require $\Phi$ to do phase retrieval for $\mathcal{H}$ in the statement of Theorem 2.7. As the following proposition shows, this is, in fact, not necessary.
Since Riesz bases never do phase retrieval, Proposition 2.9 does indeed show that a frame in an infinite-dimensional Hilbert space need not do phase retrieval itself in order to satisfy all the conditions in Theorem 2.7. On the other hand, if $\Phi$ does phase retrieval for $\mathcal{H}$ and $\{V_m\}_{m\in \mathbb{N}}$ is any sequence of finite-dimensional subspaces with increasing dimensions, then it follows from Proposition 1.4 that the pair $(\Phi ,\{V_m\}_{m\in \mathbb{N}})$ satisfies the hypotheses of Theorem 2.7 for some function $G(m)$; see also Proposition 2.10 below.
In the formulation of Theorem 2.7, we used the infinite sequences $\mathcal{A}_{\Phi }(f):= \left(|\langle f, \varphi _n \rangle | \right)_{n \in \mathbb{N}}$, for $f \in V_m$, even though this is surely overkill for elements $f$ in the finite-dimensional spaces $V_m$. As the following proposition shows, one can, at little cost, restrict the frame to an appropriate finitely-truncated set of vectors for each $V_m$: if $\{\Phi ,\{V_m\}_{m\in \mathbb{N}}\}$ satisfies Theorem 2.7 for the function $G(m)$, then there is an $N(m)$ for each $m$ so that $(\{\varphi _n\}_{n=1}^{N(m)},\{V_m\}_{m\in \mathbb{N}})$ satisfies Theorem 2.7 for some function $H(m)\geq G(m)$.
Note that in the requirement that $\{P_V\varphi _n\}_{n\in \mathbb{N}}$ does phase retrieval for $V$, i.e., up to global phase, any $f \in V$ can be reconstructed from the sequence $\left(|\langle f, P_V \varphi _n \rangle | \right)_{n \in \mathbb{N}}$, one can equally well drop the projector $P_V$, since $\langle f, P_V \varphi _n \rangle =\langle f, \varphi _n \rangle$.
The frame used in the proof of Proposition 2.9 was rather contrived, and would not be used in any concrete applications. It is reasonable to wonder how the function $G$ of Theorem 2.7 behaves for choices of $\Phi$ and $\{V_m\}$ of practical interest. It turns out that it may grow very fast, as illustrated by the following example.
Note that since the frame $\Phi$ in this example does phase retrieval for $\mathcal{H}$ (see Reference 10), it must do phase retrieval for each $V_m \subset \mathcal{H}$, meaning that (by the argument used in the proof of Proposition 2.10) there must indeed exist $N(m)$ and $H(m)$ such that, for all $f,g \in V_m$
i.e., (Equation 2.1) must be satisfied for $G(m)=\max _{1\leq k\leq m}H(k)$. The computation above tells us that, no matter how large we pick the $N(m)$,$G$ must grow at least as fast as indicated by (Equation 2.5).
Acknowledgments
The authors wish to thank the reviewer for a careful reading of the paper, pointing out several inaccuracies, and suggesting a shortcut for the proof of Theorem 2.2.
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