# On stability of generalized phase retrieval and generalized affine phase retrieval

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## Abstract

In this paper, we consider the stability of intensity measurement mappings corresponding to generalized phase retrieval and generalized affine phase retrieval in the real case. First, we show the bi-Lipschitz property on measurements of noiseless signals. After that, the stability property as regards a noisy signal is given by the Cramer–Rao lower bound.

## Keywords

Bi-Lipschitz Generalized affine phase retrieval Cramer–Rao lower bound## MSC

42C15## 1 Introduction

*x*from its intensity measurements \(| \langle x, \varphi_{i} \rangle| \), \(i=1,\ldots,N \), where \(\{\varphi_{i}\}_{i=1}^{N} \) is a frame of \(F ^{d} \). The phase retrieval problem has a long history, which can be traced back as far as 1952 [13]. It has important applications in optics, communication, X-ray crystallography, quantum tomography, signal processing and more (see e.g. [11, 14, 16] and the references therein). The task of classical phase retrieval is recovering a signal from its Fourier transform magnitude [5, 8]. Using frame theory, Balan et al. constructed a new class of Parseval frames for a Hilbert space in 2006 [2], which allows signal reconstruction from the absolute value of the frame coefficients. Since then lots of theoretical results and practical algorithms emerged in different fields. Generalized phase retrieval was introduced by Yang Wang and Zhiqiang Xu [17], including as special cases the standard phase retrieval as well as the phase retrieval by orthogonal projection. Explicitly, let \(H_{d}(F) \) denote the set of \(d \times d \) Hermitian matrices over

*F*(if \(F=\mathbb {R}\) then Hermitian matrices are symmetric matrices). As a standard phase retrieval problem, we consider the equivalence relation ∼ on \(F^{d} : x_{1} \sim x_{2} \) if there is a constant \(b \in F \) with \(|b| = 1 \) such that \(x_{1} = bx_{2} \). Let \(\underline{F}^{d}:=F^{d}/\sim\). For any given \(A = \{A_{j}\}^{N}_{j =1}\subset H_{d}(F) \), define the map \(M_{A} : F^{d}\rightarrow\mathbb{R}^{N} \) by

*x*. We say that

*A*is generalized phase retrievable if \(M_{A} \) is injective on \(\underline{F}^{d} \). Similarly, if \(A_{j} \) is positive semidefinite for \(j=1,\ldots,N \), we can define the map \(\sqrt{M_{A}} : F^{d}\rightarrow\mathbb{R}^{N} \) by

*x*from \(\{| \langle x, \varphi_{j} \rangle|\}_{j=1}^{N} \), they consider recovering

*x*from the absolute values of the affine intensity measurements

Given two vectors \(x,y \in F^{d}\), we define metrics \(d(x,y)= \Vert {x-y} \Vert \), \(d_{1}(x,y)=\min\{ \Vert {x-y} \Vert , \Vert {x+y} \Vert \} \) and matrix metric \(d_{2}(x,y)= \Vert {x+y} \Vert \Vert {x-y} \Vert \) corresponding to the nuclear norm. Several robustness bounds to the probabilistic phase retrieval problem in a real case are given in [7]. Stability bounds of a reconstruction for a deterministic frame are studied in [3, 4] with appropriate metrics.

Our study mainly focuses on the stability of generalized phase retrieval and generalized affine phase retrieval in real case in two aspects. The first one addresses the bi-Lipschitz property of generalized phase retrieval. Section 2 shows that the mappings \(M_{A} \) and \(\sqrt{M_{A}} \) all have the bi-Lipschitz property with respect to an appropriate metric. However, the generalized affine phase retrieval mappings \(M_{B,b} \) and \(\sqrt{M_{B,b}} \) only can be controlled by two metrics. The second aspect deals with the Cramer–Rao lower bound of generalized phase retrieval and generalized affine phase retrieval in an additive white Gaussian noise model. The Cramer–Rao lower bound of any unbiased estimator is given by calculating the Fisher information matrix.

## 2 Stability of generalized phase retrieval

### Lemma 2.1

*Suppose*\(A= \{A_{j}\}_{j=1}^{N} \)*is a collection of Hermitian matrices in*\(H_{d}(F) \). *Then for any*\(x \neq0 \), *the collection*\(\{A_{j}x\}_{j=1}^{N} \)*forms a frame for*\(F^{d} \)*if and only if*\(a_{0}>0 \)*and*\(b_{0}<+\infty\). *In this case*, (2.1) *holds for every*\(x,y\in F^{d}\).

For \(A=\{A_{j}\}_{j=1}^{N} \subset H_{d}(\mathbb {R}) \), Yang Wang and Zhiqiang Xu [17] proved that *A* is phase retrievable if and only if \(\{A_{j}x\}_{j=1}^{N} \) is a frame of \(\mathbb {R}^{d} \) for any nonzero \(x\in \mathbb {R}^{d} \). This incorporating Lemma 2.1 leads to the following theorem.

### Theorem 2.1

*Let*\(A=\{A_{j}\}_{j=1}^{N} \subset H_{d}(\mathbb {R}) \). *Then**A**is phase retrievable if and only if*\(a_{0}>0 \)*and*\(b_{0}<+\infty\).

Since \(| \langle A_{j}x, y \rangle|^{2}=y^{T}A_{j}xx^{T}A_{j}y \) in real case, the above theorem can be rewritten in the quadratic forms as follows.

### Corollary 2.1

*Let*\(A=\{A_{j}\}_{j=1}^{N} \subset H_{d}(\mathbb {R})\).

*Then*

*A*

*is phase retrieval if and only if there are two positive real constants*\(a_{0}\), \(b_{0} \)

*such that*

*where the inequality is in the sense of quadratic forms and*\(R_{x}:=\sum_{j=1}^{N}A_{j}xx^{T}A_{j} \).

*i*th column of the matrix \(B_{j}^{T} \), then \(A_{j}x \) can be expanded as

### Theorem 2.2

*Suppose*\(A_{j}=B_{j}^{T}B_{j}\in H_{d}(\mathbb {R}) \)*is a positive semidefinite matrix and*\(B_{j}^{T}=(b_{j,1},\ldots,b_{j,r_{j}}) \)*for*\(j=1,\ldots, N \). *If*\(\{A_{j}\}_{j=1}^{N} \)*is generalized phase retrievable*, *then the column vectors*\(\{b_{j,i}\}_{i=1,j=1}^{r_{j},N} \)*satisfy the complementary property and therefore become a phase retrievable frame*.

### Proof

*S*is an arbitrary subset of

*Λ*, it can be divided into two parts: \(S_{j}=\{i|(j,i)\in S\} \) and \(S^{C}_{j}=\{1,2,\ldots,r_{j}\}\setminus S_{j} \). If neither \(\{b_{j,i}\}_{(j,i)\in S} \) nor \(\{b_{j,i}\}_{(j,i)\in S^{C}} \) is a spanning set of \(\mathbb{R}^{N} \), then there exist two nonzero elements \(x,y\in\mathbb{R}^{d} \) such that \(\langle x, b_{j,i} \rangle=0 \) for \(he (j,i)\in S \) and \(\langle y, a_{j,i} \rangle=0 \) for \((j,i)\in S^{C} \). Consequently, for \(j=1,\ldots, N \), we have

*j*. Since \(\{A_{j}\}_{j=1}^{N} \) is phase retrievable, we have \(x+y=\pm(x-y) \). This contradicts the nonzeroness of

*x*and

*y*. □

### 2.1 Bi-Lipschitz property

In this subsection, we consider the bi-Lipschitz property of tmappings \(M_{A} \) and \(\sqrt{M_{A}} \). At first, we show the stability of the mapping \(M_{A} \) with respect to the metric \(d_{2} \).

### Theorem 2.3

*Let*\(\{A_{j}\}_{j=1}^{N} \subset H_{d} (\mathbb {R}) \)*be generalized phase retrievable*. *Then*\(M_{A} \)*is bi*-*Lipschitz with respect to matrix metric*\(d_{2}(x,y)= \Vert {x+y} \Vert \Vert {x-y} \Vert \).

### Proof

Now, we consider the stability of the mapping \(\sqrt{M_{A}} \) with respect to the metric \(d_{1} \).

### Lemma 2.2

*Let*\(\{A_{j}\}_{j=1}^{N} \subset H_{d}(\mathbb {R}) \)*be a collection of positive semidefinite matrices and generalized phase retrievable*. *Then*\(\sqrt{ M_{A}} \)*is upper bounded with respect to the metric*\(d_{1}(x,y)=\min\{ \Vert {x+y} \Vert , \Vert {x-y} \Vert \} \).

### Proof

*x*of \(\sum_{j=1}^{N}A_{j} \) corresponding to \(\lambda_{1}\), then we have

### Lemma 2.3

*Let*\(\{A_{j}\}_{j=1}^{N} \subset H_{d}^{N}(\mathbb {R}) \)*be generalized phase retrievable and positive semidefinite*. *Then*\(\sqrt{ M_{A}} \)*is lower bounded with respect to the metric*\(d_{1}(x,y)=\min\{ \Vert {x+y} \Vert , \Vert {x-y} \Vert \} \).

### Proof

*C*is the uniform upper operator bound for \(\{ A_{j}\}_{j=1}^{N} \), that is, \(\Vert {A_{j} x} \Vert \leq C \Vert {x} \Vert \) for \(x\in \mathbb {R}^{d} \) and \(j=1,\ldots,N \). Therefore \(\Vert {B_{j}x} \Vert \leq\sqrt{C} \Vert {x} \Vert \) and we have

Our discussion of the bi-Lipschitz property of \(\sqrt{M_{A}} \) is summarized in the following theorem by combining Lemma 2.2 and Lemma 2.3.

### Theorem 2.4

*Let*\(\{A_{j}\}_{j=1}^{N} \subset H_{d}^{N}(\mathbb {R}) \)

*be generalized phase retrievable and positive semidefinite*.

*Then*\(\sqrt{ M_{A}} \)

*is bi*-

*Lipschitz with respect to the metric*\(d_{1}(x,y)=\min\{ \Vert {x+y} \Vert , \Vert {x-y} \Vert \} \)

*as follows*:

Phase retrieval by projections introduced by Cahill et al. in [6] aims at recovering a signal from measurements consisting of norms of its orthogonal projections onto a family of subspaces. Since \(x^{T}P_{j}x= \Vert {P_{j}x} \Vert ^{2} \) when \(P_{j} \) is a projection to an appropriate subspace of \(\mathbb {R}^{d} \), phase retrieval by projections is a special case of generalized phase retrieval with \(A_{j}=P_{j} \). Therefore, Theorem 2.3 and Theorem 2.4 also hold for phase retrieval by projections. In this special case, \(\lambda_{1} \) can be upper bounded by *N* and *C* equals one.

### 2.2 Cramer–Rao lower bound

*Z*are independent Gaussian random variables with mean value 0 and variance \(\sigma^{2} \). The generalized phase retrieval problem with noise is to estimate

*x*from measurements

*Y*. In this case, we apply the theory of Fisher information to evaluate the stability of \(\varphi(x) \). The Fisher information matrix is defined entry-wise by

*Y*is a random vector with

*j*th component of \(\varphi(x) \). Since \(\mathbb {E}[y_{j}]=(\varphi(x))_{j} \), the second expectation equals zero and

*x*introduced in [1]: the signal

*x*is in a half super plane with respect to a vector \(e\in \mathbb {R}^{d} \), i.e. \(\langle x, e \rangle>0 \). See Ref. [4] for another assumption to guarantee uniqueness. Substituting \(\varphi(x)=(x^{T}A_{j}x)_{j=1}^{N} \) into (2.5), we have

*m*th element of vector \(A_{j}x \). This indicates the Fisher information matrix can be expressed as

### Theorem 2.5

*The Fisher information matrix for the noisy generalized phase retrieval model in real case is given by*(2.6).

*Consequently*,

*for any unbiased estimator*\(\varPhi(y) \)

*for*

*x*,

*the covariance matrix is bounded below by the Cramer–Rao lower bound as follows*:

*Therefore*,

*the mean square error of any unbiased estimator*\(\varPhi(y)\)

*is given by*

### Corollary 2.2

*If*\(A=\{A_{j}\}_{j=1}^{N} \)

*is generalized phase retrievable*,

*then*,

*for any unbiased estimator*\(\varPhi(y) \)

*for x*,

*we have*

*Furthermore*,

*any unbiased estimate that achieves the Cramer–Rao lower bound has a mean square error that is bounded above by*

## 3 Stability of generalized affine phase retrieval

The standard affine phase retrieval introduced by Bing Gao et al. in [9] can be used for recovering signals with prior knowledge. In this section, we consider generalized affine phase retrieval theoretically and give some basic mathematical properties at first, then we focus on its stability property.

*x*from the norm of the affine linear measurements \(\Vert {B_{j}x+b_{j}} \Vert \), \(j=1,\ldots,N \), where \(b_{j} \in F^{r_{j}} \) and \(x\in F^{d} \). Let \(B=\{B_{j}\}_{j=1}^{N} \) and \(b=\{b_{j}\}_{j=1}^{N} \), we define the mapping \(M_{B,b}:F^{d} \rightarrow \mathbb {R}^{N}_{+} \) by

### Theorem 3.1

*Let*\(B_{j} \in \mathbb {R}^{r_{j}\times d} \)

*and*\(b_{j}\in \mathbb {R}^{r_{j}} \).

*Then the following are equivalent*:

- (A)
*The pair*\((B,b) \)*is generalized affine phase retrievable for*\(\mathbb {R}^{d} \). - (B)
*There exist no nonzero*\(u \in \mathbb {R}^{d} \)*such that*\(\langle B_{j}u, B_{j}v+b_{j} \rangle=0 \)*for all*\(1\leq j \leq N \)*and*\(v\in \mathbb {R}^{d} \). - (C)
*If**v**is the solution of equations*\(B_{j}v+b_{j}=0 \)*for*\(j\in S\subset\{1,2,\ldots,N\} \),*then*\(\{B_{j}^{T}B_{j}v+B_{j}^{T}b_{j}\}_{j\in S^{C}} \)*is a spanning set of*\(\mathbb {R}^{d} \). - (D)
*The Jacobian of*\(M_{B,b} \)*has rank**d**everywhere on*\(\mathbb {R}^{d}\).

### Proof

*j*, we have

*j*,

*j*. Let \(x,y \in \mathbb {R}^{d} \) be given by \(x-y=2u \) and \(x+y=2v \). Then \(x\neq y \). However, we have \(M_{B,b}(x)=M_{B,b}(y) \). Hence \((B,b) \) cannot be affine phase retrievable.

(B) ⇔ (C). Assume \(\{B_{j}^{T}B_{j}v+B_{j}^{T}b_{j}\}_{j\in S^{C}} \) is not a spanning set of \(\mathbb {R}^{d} \), then there is a nonzero vector \(u\in \mathbb {R}^{d} \) such that \(\langle B_{j}u, B_{j}v+b_{j} \rangle= \langle u, B_{j}^{T}B_{j}v+B_{j}^{T}b_{j} \rangle=0 \) for \(j\in S^{C} \). For \(j\in S \), since *v* is the solution of equations \(B_{j}v+b_{j}=0 \), the inner product \(\langle B_{j}u, B_{j}v+b_{j} \rangle \) also equals zero, which contradicts (B). The converse can be proven similarly.

*x*is exactly

*j*th column of \(J_{B,b} \) is precisely \(B_{j}^{T}B_{j}x+B_{j}^{T}b_{j} \). This indicates the equivalence of (C) and (D). □

Minimality problems have attracted much attention from different areas recently. For generalized affine phase retrieval, the answer is related to different constraints on \(B_{j} \), \(b_{j} \) and prior knowledge of signal *x*. The following theorem is given in [10].

### Theorem 3.2

([10])

*Let*\(N\geq2d \)*and*\(r>1 \). *Then a generic*\(\{(B_{j},b_{j})\}_{j=1}^{N} \subset \mathbb {R}^{r\times(d+1)} \)*has the generalized affine phase retrieval property in*\(\mathbb {R}^{d} \).

Let \(r=\max_{j} r_{j} \). The \(r_{j}\times(d+1) \) matrix \((B_{j},b_{j}) \) in Theorem 3.1 can be extended to \(r\times(d+1) \) matrix by filling with zero rows. The extended matrix can be viewed as an affine phase retrieval matrix where all \(r_{j}=r \) and hence leads to the following corollary by Theorem 3.2.

### Corollary 3.1

*Let*\(\tilde{A}_{j}=(B_{j}^{T},b_{j}^{T})^{T}(B_{j},b_{j})\), *where*\(b_{j}\in \mathbb {R}^{r_{j}} \)*and*\(B_{j}\in \mathbb {R}^{r_{j}\times d} \)*is a nonzero matrix*. *If*\(N\geq2d \)*and*\(\tilde{A}=(\tilde{A}_{j})_{j=1}^{N} \)*is a generic set in*\(H_{d}^{N}(\mathbb {R}) \), *Then the pair*\((B,b) \)*is generalized affine phase retrievable*.

### Example 3.1

*x*,

*y*. The number of measurements equals 3 with \(r_{1}=r_{2}=2\), \(r_{3}=1 \).

Now, we consider the stability of generalized affine phase retrieval. Let \(\tilde{B}_{j}=(B_{j},b_{j}) \), \(\tilde{A}_{j}=\tilde{B}_{j}^{T}\tilde{B}_{j} \), and \(\tilde{x}=(x^{T},1)^{T} \). We have the following theorem.

### Theorem 3.3

*Suppose*\(\tilde{A}=\{\tilde{A}_{j}\}_{j=1}^{N} \)

*is a generic set with*\(N\geq2d \),

*then*\((B, b) \)

*is generalized affine phase retrievable*.

*Furthermore*,

*there exist positive constants*\(c_{0}\), \(c_{1}\), \(C_{0}\), \(C_{1} \)

*depending on*\((B, b)\)

*such that*,

*for any*\(x,y \in\mathbb{R}^{d} \),

### Proof

In contrast to Theorem 4.1 in [9], Theorem 3.4 leads to a slack constraint of the signal from a compact set to \(\mathbb{R}^{d} \). Although affine phase retrieval is not bi-Lipschitz with respect to one metric, the mappings \(M_{B,b} \) and \(\sqrt{M_{B,b}} \) is bounded by two metrics.

### Lemma 3.1

*If the pair*\((B,b) \)*is generalized affine phase retrievable*, *then the Fisher information*\(R_{x}^{a} \)*is positive definite for any*\(x\in \mathbb {R}^{d} \).

### Proof

Similar to generalized phase retrieval, we have the following theorem.

### Theorem 3.4

*The Fisher information matrix for the noisy generalized affine phase retrieval model in real case is*\(\frac{4}{\sigma^{2}}R_{x}^{a} \).

*Consequently*,

*for any unbiased estimator*\(\varPhi(y) \)

*for*

*x*,

*The covariance matrix is bounded below by the Cramer–Rao lower bound as follows*:

*Therefore*,

*the mean square error of any unbiased estimator*\(\varPhi(y)\)

*is given by*

*c*and

*C*such that

### Lemma 3.2

*If the pair*\((B,b) \)*is generalized affine phase retrievable*, *then the collection*\(\{B_{j}^{T}B_{j}\}_{j=1}^{N} \)*is a g*-*frame for*\(\mathbb {R}^{d} \).

### Proof

### Corollary 3.2

*If the pair*\((B,b) \)

*is generalized affine phase retrievable*,

*then*,

*for any unbiased estimator*\(\varPhi(y) \)

*for nonzero*

*x*,

*we have*

### Proof

We discussed the stability of generalized phase retrieval and affine generalized phase retrieval in this paper. The first one can be viewed as a generalization of stability of phase retrieval in [3, 4], or as a continuation of the work in [17]. The second one is an extension of the work in [9, 10]. As all the results in this paper are obtained in real Hilbert space, the stability property in complex Hilbert space still needs to be addressed.

## Notes

### Acknowledgements

The authors would like to thank the referees for their useful comments and remarks.

### Availability of data and materials

Not applicable.

### Authors’ contributions

All authors contributed equally to this work. All authors read and approved the final manuscript.

### Funding

This study was partially supported by National Natural Science Foundation of China (Grant No. 11601152).

### Competing interests

The authors declare that they have no competing interests.

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