Blind Super-resolution via Projected Gradient Descent

by   Sihan Mao, et al.

Blind super-resolution can be cast as low rank matrix recovery problem by exploiting the inherent simplicity of the signal. In this paper, we develop a simple yet efficient nonconvex method for this problem based on the low rank structure of the vectorized Hankel matrix associated with the target matrix. Theoretical guarantees have been established under the similar conditions as convex approaches. Numerical experiments are also conducted to demonstrate its performance.



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1 Introduction

Blind super-resolution of point sources is the problem of simultaneously estimating locations and amplitudes of point sources and point spread functions from low-resolution measurements. Such problem arises in various applications, including single-molecule imaging

[13], medical imaging [12], multi-user communication system [10] and so on.

Under certain subspace assumption and applying the lifting technique, blind super-resolution can be cast as a matrix recovery problem. Recent works in [7, 17, 9, 15, 5] exploit the intrinsic structures of data matrix and propose different convex relaxation methods for such problem. Theoretical guarantees for these methods have been established. However, due to the limitations of convex relaxation, all of these methods do not scale well to the high dimensional setting.

In contrast to convex relaxation, a non-convex recovery method is proposed in this paper based on the Vectorized Hankel Lift [5] framework. More precisely, harnessing low-rank structure of vectorized Hankel matrix corresponding to the signal in terms of the Burer-Monteiro factorization, we develop a projected gradient descent algorithm, named PGD-VHL, to directly recover the low rank factors. We show that such a simple algorithm possesses a remarkable reconstruction ability. Moreover, our algorithm started from an initial guess converges linearly to the target matrix under the similar sample complexity as convex approaches.

The rest of this paper is organized as follows. We begin with the problem formulation and describe the proposed algorithm in Section 2. Section 3 provides a convergence analysis of PGD-VHL. Numerical experiments to illustrate the performance of PGD-VHL are provided in Section 4. Section 5 concludes this paper.

2 Algorithm

2.1 Problem formulation

The point source signal can be represented as a superposition of spikes

where and are the amplitude and location of -th point source respectively. Let be the unknown point spread functions. The observation is a convolution between and ,

After taking Fourier transform and sampling, we obtain the measurements as


Let be a vector corresponding to the -th unknown point spread function. The goal is to estimate as well as from (2.1). Since the number of unknowns is larger than , this problem is an ill-posed problem without any additional assumptions. Following the same route as that in [7, 17, 9, 5], we assume belong to a known subspace spanned by the columns of , i.e.,

Then under the subspace assumption and applying the lifting technique [1], the measurements (2.1) can be rewritten as a linear observations of :


where is the -th row of , is the -th standard basis of , and is the vector defined as

The measurement model (2.2) can be rewritten succinctly as


where is the linear operator. Therefore, blind super-resolution can be cast as the problem of recovering the data matrix from its linear measurements (2.3).

Let be the vectorized Hankel lifting operator which maps a matrix into an matrix,

where is the -th column of and . It is shown that is a rank- matrix [5] and thus the matrix admits low rank structure when . It is natural to recover by solving the constrained least squares problem

To introduce our algorithm, we assume that is -incoherent which is defined as below.

Assumption 2.1.


be the singular value decomposition of

, where and . Denote , where is the -th block of for . The matrix is -incoherence if and obey that

for some positive constant .

Remark 2.1.

Assumption 2.1 is the same as the one made in [4, 18] for low rank matrix recovery and is used in blind super-resolution [5]. It has been established that Assumption 2.1 is obeyed when the minimum wrap-up distance between the locations of point sources is greater than about .

Letting and , we have

where . Since that target data matrix is -incoherence and the low rank structure of the vectorized Hankel matrix can be promoted by , it is natural to recover the low rank factors of the ground truth matrix by solving an optimization problem in the form of


where , is the Moore-Penrose pseudoinverse of obeying that , the second term in objective guarantees that admits vectorized Hankel structure. Last term penalizes the mismatch between and , which is widely used in rectangular low rank matrix recovery [16, 19, 6]. The convex feasible set is defined as follows


where is the -th block of , and be two absolute constants such that and .

2.2 Projected gradient descent method

Inspired by [2], we design a projected gradient descent method for the problem (2.1), which is summarized in Algorithm 1.

while  not convergence do
end while
Algorithm 1 PGD-VHL

The initialization involves two steps: (1) computes the best rank approximation of via one step hard thresholding , where is the adjoint of ;(2) projects the low rank factors of best rank- approximated matrix onto the set . Given a matrix , the projection onto , denoted by , has a closed form solution:

for and

for . Let be the current estimator. The algorithm updates along gradient descent direction with step size , followed by projection onto the set . The gradient of is computed with respect to Wirtinger calculus given by where

To obtain the computational cost of , we first introduce some notations. Let be the Hankel operator which maps a vector into an matrix,

where is the -th entry of . The adjoint of , denoted by , is a linear mapping from to . It is known that the computational complexity of both and is flops [3]. Moreover, the authors in [5] show that , where is a matrix constructed by stacking all on top of one another, and is a permutation matrix. Therefore we can compute and by using flops. Thus the implementation of our algorithm is very efficient and the main computational complexity in each step is .

3 Main results

In this section, we provide an analysis of PGD-VHL under a random subspace model.

Assumption 3.1.

The column vectors of are independently and identically drawn from a distribution which satisfies the following conditions

Remark 3.1.

Assumption 3.1 is a standard assumption in blind super-resolution [7, 17, 9, 14, 5], and holds with by many common random ensembles, for instance, the components of

are Rademacher random variables taking the values

with equal probability or

is uniformly sampled from the rows of a Discrete Fourier Transform (DFT) matrix.

Now we present the main result of the paper.

Theorem 3.1.

Let and for . Let and . Suppose obeys the Assumption 2.1 and the subspace satisfies the Assumption 3.1. If

with probability at least , the sequence returned by Algorithm 1 satisfies


where are absolute constants, , is the condition number of , and the distance is defined as

Remark 3.2.

The detailed proof of Theorem 3.1 is provided in [11]. Compared with the sample complexity established in [5] for the nuclear norm minimization method, which is , Theorem 3.1 implies that PGD-VHL is sub-optimal in terms of and . We suspect that it is merely an artifact of our proof.

Remark 3.3.

Theorem 3.1 implies that PGD-VHL converges to with a linear rate. Therefore, after iterations, we have . Given the iterates returned by PGD-VHL, we can estimate by .

Remark 3.4.

Once the data matrix is recovered, the locations can be computed from by MUSIC algorithm and the weights can be estimated by solving an overdetermined linear system [5].

4 Numerical simulations

In this section, we provide numerical results to illustrate the performance of PGD-VHL. The locations of the point source signal is randomly generated from and the amplitudes are selected to be , where is uniformly sampled from and is uniformly sampled from . The coefficients are i.i.d. sampled from standard Gaussian with normalization. The columns of are uniformly sampled from the DFT matrix. The stepsize of PGD-VHL is chosen via backtracking line search and PGD-VHL will be terminated if is met or a maximum number of iterations is reached. We repeat 20 random trials and record the probability of successful recovery in our tests. A trial is declared to be successful if .

The first experiment studies the recovery ability of PGD-VHL through the framework of phase transition and we compare it with two convex recovery methods: VHL

[5] and ANM [17]. Both VHL and ANM are solved by CVX [8]. The tests are conducted with and the varied and . Figure 1(a), 1(c) and 1(e) show that phase transitions of VHL, ANM and PGD-VHL when the locations of point sources are randomly generated, and Figure 1(b), 1(d) and 1(f) illustrate the phase transitions of VHL, ANM and PGD-VHL when the separation condition is imposed. It can be seen that PGD-VHL is less sensitive to the separation condition than ANM and has a higher phase transition curve than VHL.

(a) (b) (c) (d) (e) (f)
Figure 1: The phase transitions of VHL (a,b), ANM (c,d) and PGD-VHL (e,f) with (Left) or without (Right) imposing the separation condition. Here we fix . The red curve plots the hyperbola curve .

In the second experiment, we study the phase transition of PGD-VHL when one of and is fixed. Figure 2(a) indicates a approximately linear relationship between and for the successful recovery when the number of point sources is fixed to be . The same linear relationship between and can be observed when the dimension of the subspace is fixed to be , see Figure 2(b). Therefore there exists a gap between our theory and empirical observation and we leave it as future work.

(a) (b)
Figure 2: (a) The phase transition of PGD-VHL for varying and when . The red line plots the straight line . (b) The phase transition of PGD-VHL for varying and when . The red line plots the straight line .

In the third simulation, we investigate the convergence rate of PGD-VHL for different . The results are shown in Figure 3. The -axis denotes and the -axis represents the iteration number. It can be clearly seen that PGD-VHL converges linearly as shown in our main theorem.

Figure 3: Convergence of PGD-VHL for . Here we fix and .

5 Conclusion

In this paper, we propose an efficient algorithm named PGD-VHL towards recovering low rank matrix in blind super-resolution. Our theoretical analysis shows that the proposed algorithm converges to the target matrix linearly. This is also demonstrated by our numerical simulations.


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