Abstract
We advocate a compressed sensing strategy that consists of multiplying the signal of interest by a wide bandwidth modulation before projection onto randomly selected vectors of an orthonormal basis. First, in a digital setting with random modulation, considering a whole class of sensing bases including the Fourier basis, we prove that the technique is universal in the sense that the required number of measurements for accurate recovery is optimal and independent of the sparsity basis. This universality stems from a drastic decrease of coherence between the sparsity and the sensing bases, which for a Fourier sensing basis relates to a spread of the original signal spectrum by the modulation (hence the name "spread spectrum"). The approach is also efficient as sensing matrices with fast matrix multiplication algorithms can be used, in particular in the case of Fourier measurements. Second, these results are confirmed by a numerical analysis of the phase transition of the ℓ_{1}minimization problem. Finally, we show that the spread spectrum technique remains effective in an analog setting with chirp modulation for application to realistic Fourier imaging. We illustrate these findings in the context of radio interferometry and magnetic resonance imaging.
1 Introduction
In this section we concisely recall some basics of compressed sensing, emphasizing on the role of mutual coherence between the sparsity and sensing bases. We discuss the interest of improving the standard acquisition strategy in the context of Fourier imaging techniques such as radio interferometry and magnetic resonance imaging (MRI). Finally we highlight the main contributions of our study advocating a universal and efficient compressed sensing strategy coined spread spectrum, and describe the organization of this article.
1.1 Compressed sensing basics
Compressed sensing is a recent theory aiming at merging data acquisition and compression
[17]. It predicts that sparse or compressible signals can be recovered from a small number
of linear and nonadaptative measurements. In this context, Gaussian and Bernouilli
random matrices, respectively with independent standard normal and ± 1 entries, have
encountered a particular interest as they provide optimal conditions in terms of the
number of measurements needed to recover sparse signals [35]. However, the use of these matrices for realworld applications is limited for several
reasons: no fast matrix multiplication algorithm is available, huge memory requirements
for large scale problems, difficult implementation on hardware, etc. Let us consider
ssparse digital signals x ∈ ℂ^{N }in an orthonormal basis ψ = (ψ_{1},...,ψ_{N}) ∈ ℂ^{N×N}. The decomposition of x in this basis is denoted
We also denote A = ϕ* ψ ∈ ℂ^{N×N}. Finally we aim at recovering α by solving the ℓ_{1}minimization problem
where
The theory of compressed sensing already demonstrates that a small number m ≪ N of random measurements are sufficient for an accurate and stable reconstruction of x [6,7]. However, the recovery conditions depend on the mutual coherence μ between ϕ and ψ. This value is a similarity measure between the sensing and sparsity bases. It is defined as μ = max_{1 ≤ i,j ≤ N }〈ϕ_{i},ψ_{j}〉 and satisfies N^{1/2 }≤ μ ≤ 1. The performance is optimal when the bases are perfectly incoherent, i.e., μ = N^{1/2}, and unavoidably decreases when μ increases.
1.2 Fourier imaging applications and mutual coherence
The dependence of performance on the mutual coherence μ is a key concept in compressed sensing. It has significant implications for Fourier imaging applications, in particular radio interferometry or MRI, where signals are probed in the orthonormal Fourier basis. In radio interferometry, one of the main challenges is to reconstruct accurately the original signal from a limited number of accessible measurements [812]. In MRI, accelerating the acquisition process by reducing the number of measurements is of huge interest in, for example, static and dynamic imaging [1317], parallel MRI [1820], or MR spectroscopic imaging [2123]. The theory of compressed sensing shows that Fourier acquisition is the best sampling strategy when signals are sparse in the Dirac basis. The sensing system is indeed optimally incoherent. Unfortunately, natural signals are usually rather sparse in multiscale bases, e.g., wavelet bases, which are coherent with the Fourier basis. Many measurements are thus needed to reconstruct accurately the original signal. In the perspective of accessing better performance, sampling strategies that improve the incoherence of the sensing scheme should be considered.
1.3 Main contributions and organization
In the present study, we advocate a compressed sensing strategy coined spread spectrum that consists of a wide bandwidth premodulation of the signal x before projection onto randomly selected vectors of an orthonormal basis. In the particular case of Fourier measurements, the premodulation amounts to a convolution in the Fourier domain which spreads the power spectrum of the original signal x (hence the name "spread spectrum"), while preserving its norm. Equivalently, this spread spectrum phenomenon acts on each sparsity basis vector describing x so that information of each of them is accessible whatever the Fourier coefficient selected. This effect implies a decrease of coherence between the sparsity and sensing bases and enables an enhancement of the reconstruction quality.
In Section 2, we study the spread spectrum technique in a digital setting for arbitrary
pairs of sensing and sparsity bases (ϕ, ψ). We consider a digital premodulation
where ϕ_{ki }and ψ_{kj }are respectively the kth entries of the vectors ϕ_{i }and ψ_{j}. We then show that this parameter reaches its optimal value β (ϕ, ψ) = N^{1/2 }whatever the sparsity basis Ψ, for particular sensing matrices ϕ including the Fourier matrix, thus providing universal recovery performances. It is also efficient as sensing matrices with fast matrix multiplication algorithms can be used, thus reducing the need in memory requirement and computational power. In Section 3, these theoretical results are confirmed numerically through an analysis of the empirical phase transition of the ℓ_{1}minimization problem for different pairs of sensing and sparsity bases. In Section 4, we show that the spread spectrum technique remains effective in an analog setting with chirp modulation for application to realistic Fourier imaging, and illustrate these findings in the context of radio interferometry and MRI. Finally, we conclude in Section 5.
In the context of compressed sensing, the spread spectrum technique was already briefly introduced by the authors for compressive sampling of pulse trains in [24], applied to radio interferometry in [25,26] and to MRI in [2730]. This article provides theoretical foundations for this technique, both in the digital and analog settings. Note that other acquisition strategies can be related to the spread spectrum technique as discussed in Section 2.5.
Let us also acknowledge that spread spectrum techniques are very popular in telecommunications. For example, one can cite the direct sequence spread spectrum (DSSS) and the frequency hopping spread spectrum (FHSS) techniques. The former is sometimes used over wireless local area networks, the latter is used in Bluetooth systems [31]. In general, spread spectrum techniques are used for their robustness to narrowband interference and also to establish secure communications.
2 Compressed sensing by spread spectrum
In this section, we first recall the standard recovery conditions of sparse signals randomly sampled in a bounded orthonormal system. These recovery results depend on the mutual coherence μ of the system. Hence, we study the effect of a random premodulation on this value and deduce recovery conditions for the spread spectrum technique. We finally show that the number of measurements needed to recover sparse signals becomes universal for a family of sensing matrices ϕ which includes the Fourier basis.
2.1 Recovery results in a bounded orthonormal system
For the setting presented in Section 1, the theory of compressed sensing already provides sufficient conditions on the number of measurements needed to recover the vector α from the measurements y by solving the ℓ_{1}minimization problem (2) [6,7].
Theorem 1 ([7], Theorem 4.4). Let
then α is the unique minimizer of the ℓ_{1}minimization problem (2) with probability at least
Let us acknowledge that even if the measurements are corrupted by noise or if α is nonexactly sparse, the theory of compressed sensing also shows that the reconstruction obtained by solving the ℓ_{1}minimization problem remains accurate and stable:
Theorem 2 ([7], Theorem 4.4). Let A = ϕ*ψ, Ω = {l_{1},...,l_{m}} be a set of m indices chosen independently and uniformly at random from {1,...,N}, and
satisfies
with probability at least
In the above theorems, the role of the mutual coherence μ is crucial as the number of measurements needed to reconstruct x scales quadratically with its value. In the worst case where ϕ and ψ are identical, μ = 1 and the signal x is probed in a domain where it is also sparse. According to relation (4), the number of measurements necessary to recover x is of order N. This result is actually very intuitive. For an accurate reconstruction of signals sampled in their sparsity domain, all the nonzero entries need to be probed. It becomes highly probable when m ≃ N. On the contrary, when ϕ and ψ are as incoherent as possible, i.e., μ = N^{1/2}, the energy of the sparsity basis vectors spreads equally over the sensing basis vectors. Consequently, whatever the sensing basis vector selected, one always gets information of all the sparsity basis vectors describing the signal x, therefore reducing the need in the number of measurements. This is confirmed by relation (4) which shows that the number of measurements is of the order of s when μ = N^{1/2}. To achieve much better performance when the mutual coherence is not optimal, one would naturally try to modify the measurement process to achieve a better global incoherence. We will see in the following section that a simple random premodulation is an efficient way to achieve this goal whatever the sparsity matrix ψ.
2.2 Premodulation effect on the mutual coherence
The spread spectrum technique consists of premodulating the signal x by a wideband signal
where the additional matrix C ∈ ℝ^{N×N }stands for the diagonal matrix associated to the sequence c.
In this setting, the matrix A^{c }is orthonormal. Therefore, the recovery condition of sparse signals sampled with this matrix depends on the mutual coherence μ = max_{1 ≤ i,j ≤ N }〈ϕ_{i}, C ψ_{j}〉. With a premodulation by a random Rademacher or Steinhaus sequence, Lemma 1 shows that the mutual coherence μ is essentially bounded by the moduluscoherence β (ϕ, ψ) defined in Equation (3).
Lemma 1. Let ϵ ∈ (0, 1), c ∈ ℂ^{N }be a random Rademacher or Steinhaus sequence and C ∈ ℂ^{N×N }be the associated diagonal matrix. Then, the mutual coherence μ = max_{1 ≤ i,j ≤ N }〈 ϕ_{i}, C ψ_{j}〉 satisfies
with probabilty at least 1  ϵ.
The proof of Lemma 1 relies on a simple application of the Hoeffding's inequality and the union bound.
Proof. We have
for all u > 0 and 1 ≤ i, j ≤ N, with
for all u > 0. As
for all u > 0.Taking
2.3 Sparse recovery with the spread spectrum technique
Combining Theorem 1 with the previous estimate on the mutual coherence, we can state the following theorem:
Theorem 3. Let c ∈ ℂ^{N}, with N ≥ 2, be a random Rademacher or Steinhaus sequence, C ∈ ℂ^{N×N }be the diagonal matrix associated to c, α ∈ ℂ^{N }be an ssparse vector, Ω = {l_{1},...,l_{m}} be a set of m indices chosen independently and uniformly at random from {1,...,N}, and
then α is the unique minimizer of the ℓ_{1}minimization problem (2) with probability at least
Proof. It is straightforward to check that C*C = CC* = I, where I is the identity matrix. The matrix A^{c }= ϕ*Cψ is thus orthonormal and Theorem 1 applies. To keep the notations simple, let F denotes the event of failure of the ℓ_{1}minimization problem (2), X be the event of m ≥ CNμ^{2 }s log^{4 }(N), and Y be the event of
We will see, at the end of this proof, that for a proper choice of ϵ, when condition (9) holds, we have
Using this fact, we compute the probability of failure
where X^{c }denotes the complement of event X. In the first inequality, the probability
The probability of failure is thus bounded above by
Finally, noticing that for ϵ = N^{ρ }with N ≥ 2, condition (10) always holds when condition (9), with C_{ρ }= 2(3 + ρ)C, is satisfied, terminates the proof.
Note that relation (9) also ensures the stability of the spread spectrum technique relative to noise and compressibility by combination of Theorem 2 and Lemma 1.
2.4 Universal sensing bases with ideal moduluscoherence
Theorem 3 shows that the performance of the spread spectrum technique is driven by the moduluscoherence β(ϕ, ψ). In general the spread spectrum technique is not universal and the number of measurements required for accurate reconstructions depends on the value of this parameter.
Definition 1. (Universal sensing basis) An orthonormal basis ϕ ∈ ℂ^{N×N }is called a universal sensing basis if all its entries ϕ_{ki}, 1 ≤ k,i ≤ N, are of equal complex magnitude.
For universal sensing bases, e.g., the Fourier transform or the Hadamard transform, we have ϕ_{ki} = N^{1/2 }for all 1 ≤ k, i ≤ N. It follows that β (ϕ, ψ) = N^{1/2 }and μ ≃ N^{1/2}, i.e., its optimal value up to a logarithmic factor, whatever the sparsity matrix considered! For such sensing matrices, the spread spectrum technique is thus a simple and efficient way to render a system incoherent independently of the sparsity matrix.
Corollary 1. (Spread spectrum universality) Let c ∈ ℂ^{N}, with N ≥ 2, be a random Rademacher or Steinhaus sequence, C ∈ ℂ^{N×N }be the diagonal matrix associated to c, α ∈ ℂ^{N }be an ssparse vector, Ω = {l_{1},...,l_{m}} be a set of m indices chosen independently and uniformly at random from
then α is the unique minimizer of the ℓ_{1}minimization problem (2) with probability at least
For universal sensing bases, the spread spectrum technique is thus universal: the recovery condition does not depend on the sparsity basis and the number of measurements needed to reconstruct sparse signals is optimal in the sense that it is reduced to the sparsity level s. The technique is also efficient as the premodulation only requires a samplebysample multiplication between x and c. Furthermore, fast multiplication matrix algorithms are available for several universal sensing bases such as the Fourier or Hadamard bases.
In light of Corollary 1, one can notice that sampling sparse signals in the Fourier basis is a universal encoding strategy whatever the sparsity basis ψ  even if the original signal is itself sparse in the Fourier basis! We will confirm these results experimentally in Section 3.
2.5 Related work
Let us acknowledge that the techniques proposed in [3237] can be related to the spread spectrum technique. The benefit of a random premodulation in the measurement system is already briefly suggested in [32]. The proofs of the claims presented in that conference paper have very recently been accepted for publication in [33] during the review process of this article. The authors obtain similar recovery results as those presented here. In [34], the author proposes to convolve the signal x with a random waveform and randomly undersample the result in timedomain. The random convolution is performed through a random premodulation in the Fourier domain and the signal thus spreads in timedomain. In our setting, this method actually corresponds to taking ϕ as the Fourier matrix and ψ as the composition of the Fourier matrix and the initial sparsity matrix. In [35], the authors propose a technique to sample signals sparse in the Fourier domain. They first premodulate the signal by a random sequence, then apply a lowpass antialiasing filter, and finally sample it at low rate. Finally, random premodulation is also used in [36,37] but for dimension reduction and low dimensional embedding.
We recover similar results, albeit in a different way. We also have a more general interpretation. In particular, we proved that changing the sensing matrix from the Fourier basis to the Hadamard does not change the recovery condition (11).
3 Numerical simulations
In this section, we confirm our theoretical predictions by showing, through a numerical analysis of the phase transition of the ℓ_{1}minimization problem, that the spread spectrum technique is universal for the Fourier and Hadamard sensing bases.
3.1 Settings
For the first set of simulations, we consider the Dirac, Fourier, and Haar wavelet bases as sparsity basis ψ and choose the Fourier basis as the sensing matrix ϕ. We generate complex ssparse signals of size N = 1,024 with s ∈ {1,...,N}. The positions of the nonzero coefficients are chosen uniformly at random in {1,...,N}, their phases are set by generating a Steinhaus sequence, and their amplitudes follow a uniform distribution over [0, 1]. The signals are then probed according to relation (1) or (7) and reconstructed from different number of measurements m ∈ {s,...,10s} by solving the ℓ_{l}minimization problem (2) with the SPGL1 toolbox [38,39]. For each pair (m, s), we compute the probability of recovery^{a }over 100 simulations.
For the second set of simulations, the same protocol is applied with the same sparsity basis but with the Hadamard basis as the sensing matrix ϕ.
3.2 Results
Figure 1 shows the phase transitions of the ℓ_{1}minimization problem obtained for sparse signals in the Dirac, Haar, and Fourier sparsity bases and probed in the Fourier basis with and without random premodulation. Figure 2 shows the same graphs but with measurements performed in the Hadamard basis. In the absence of premodulation, one can note that the phase transitions depend on the mutual coherence of the system as predicted by Theorem 1. For the pairs FourierDirac and HadamardDirac, the mutual coherence is optimal and the experimental phase transitions match the one of DonohoTanner (dashed green line) [5]. For all the other cases, the coherence is not optimal and the region where the signals are recovered is much smaller. The worst case is obtained for the pair FourierFourier for which μ = 1. In the presence of premodulation, Corollary 1 predicts that the performance should not depend on the sparsity basis and should become optimal. It is confirmed by the phases transition showed on Figures 1 and 2 as they all match the phase transition of DonohoTanner, even for the pair FourierFourier!
Figure 1. Phase transition of the ℓ_{1}minimization problem for different sparsity bases and random selection of Fourier measurements without (left panels) and with (right panels) random modulation. The sparsity bases considered are the Dirac basis (top), the Haar wavelet basis (center), and the Fourier basis (bottom). The dashed green line indicates the phase transition of DonohoTanner [5]. The color bar goes from white to black indicating a probability of recovery from 0 to 1.
Figure 2. Phase transition of the ℓ_{1}minimization problem for different sparsity bases and random selection of Hadamard measurements without (left panels) and with (right panels) random modulation. The sparsity bases considered are the Dirac basis (top), the Haar wavelet basis (center), and the Fourier basis (bottom). The dashed green line indicates the phase transition of DonohoTanner [5]. The color bar goes from white to black indicating a probability of recovery from 0 to 1.
4 Application to realistic Fourier imaging
In this section, we discuss the application of the spread spectrum technique to realistic analog Fourier imaging such as radio interferometric imaging or MRI. Firstly, we introduce the exact sensing matrix needed to account for the analog nature of the imaging problem. Secondly, while our original theoretical results strictly hold only in a digital setting, we derive explicit performance guarantees for the analog version of the spread spectrum technique. We also confirm on the basis of simulations that the spread spectrum technique drastically enhances the quality of reconstructed signals.
4.1 Sensing model
Radio interferometry dates back to more than 60 years ago [4043]. It allows observations of the sky with angular resolutions and sensitivities inaccessible with a single telescope. In a few words, radio telescope arrays synthesize the aperture of a unique telescope whose size would be the maximum projected distance between two telescopes of the array on the plane perpendicular to line of sight. Considering small field of views, the signal probed can be considered as a planar image on the plane perpendicular to the pointing direction of the instrument. Measurements are obtained through correlation of the incoming electric fields between each pair of telescopes. As showed by the van CittertZernike theorem [43], these measurements correspond to the Fourier transform of the image multiplied by an illumination function. In general, the number of spatial frequencies probed are much smaller than the number of frequencies required by the NyquistShannon theorem, so that the Fourier coverage is incomplete. An illposed inverse problem is thus defined for reconstruction of the original image. To address this problem, approaches based on compressed sensing have recently been developed [1012].
Magnetic resonance images are created by nuclear magnetic resonance in the tissues to be imaged. Standard MR measurements take the form of Fourier (also called kspace) coefficients of the image of interest. These measurements are obtained by application of linear gradient magnetic fields that provides the Fourier coefficient of the signal at a spatial frequency proportional the gradient strength and its duration of application. Accelerating the acquisition process, or equivalently increasing the achievable resolution for a fixed acquisition time, is of major interest for MRI applications. To address this problem, recent approaches based on compressed sensing seek to reconstruct the signal from incomplete information. In this context, several approaches have been designed [13,2830,4448].
In light of the results of Section 2, Fourier imaging is a perfect framework for the
spread spectrum technique, apart from the analog nature of the corresponding imaging
problems. In the quoted applications, the random premodulation is replaced by a linear
chirp premodulation [2530]. In radio interferometry, this modulation is inherently part of the acquisition process
[25,26]. In MRI, it is easily implemented through the use of dedicated coils or RF pulses
[29,30]. For twodimensional signals, the linear chirp with chirp rate w ∈ ℝ reads as a complexvalued function
In this setting, the complete linear relationship between the signal and the measurements is given by
In the above equation, the matrix U represents an upsampling operator needed to avoid any aliasing of the modulated signal
due to a lack of sampling resolution in a digital description of the originally analog
problem. The convolution in Fourier space induced by the analog modulation implies,
in contrast with the digital setting studied before, that the band limit of the modulated
signal is the sum of the individual band limits of the original signal and of the
chirp c. We assume here that, on its finite field of view L, the signal x is approximately bandlimited with a cutoff frequency at B, i.e., its energy beyond the frequency B is negligible. The signal x is thus discretized on a grid of N = 2LB points. On this field of view L, the linear chirp c may be approximated by a band limited function of band limit identified by its maximum
instantaneous frequency wL/2. This band limit can also be parametrized in terms of a discrete chirp rate
4.2 Illustration
Up to the introduction of the matrix U and the substitution of the linear chirp modulation for the random modulation, we
are in the same setting as the one studied in Section 2. To illustrate the effectiveness
of the spread spectrum technique, we consider two images of size N = 256 × 256 showed in Figure 3. The first image shows the radio emission associated with the encounter of a galaxy
with its northern neighbor. It was acquired with the very large array in New Mexico
[49]. The second image shows a brain acquired in an MRI scanner. This image is part of
the BRAINIX database [50]. These images are probed according to relation (12) in the absence (
Figure 3. Top panels: Image of the giant elliptical galaxy NGC1316 (center of the image) devouring its small northern neighbor. The image shows the radio emission associated with this encounter superimposed on an optical image. The radio emission was imaged using the very large array in New Mexico (Image courtesy of NRAO/AUI and Uson). The image size is N = 256 × 256. Bottom panels: MRI image of a brain from the BRAINIX database. From left to right: original image; complex magnitudes of the reconstructed images from m = 0.4N measurements without chirp modulation; complex magnitudes of the reconstructed images from m = 0.4N measurements with chirp modulation.
In the absence of linear chirp modulation, the quality of the reconstructed image is very low. However, one can already note that the fine scale structures are much better reconstructed than those at large scales. The fine details live at the small scales of the wavelet decomposition whereas the large structures live at larger scales. The small scale wavelets being more incoherent with the Fourier basis than the larger wavelets, the high frequency details are naturally better recovered.
In the presence of the linear chirp modulation, all the wavelets in ψ become optimally incoherent with the Fourier basis thanks to the universality of the spread spectrum technique. Consequently, as one can observe on Figure 3, the low and high frequency details are better reconstructed and the image quality is drastically enhanced. Note that much better reconstructions can be obtained for the brain image by substituting the total variation norm^{c }for the ℓ_{1 }norm in (5) [13,30]. However, Theorems 1 and 3 do not hold for such a norm.
Let us acknowledge that these simulations are not fully realistic. For example, in radiointerferometry the spatial frequency cannot be chosen at random. To simulate realistic acquisitions, one would have to consider nonrandom measurements in the continuous Fourier plane. Such a study is beyond the scope of this study. However, in the context of MRI, part of the authors implemented and tested this technique on a real scanner with in vivo acquisitions [29,30].
4.3 Modified recovery condition
Because of the modifications introduced in (12) to account for the analog nature of the problem, the digital theory associated with the measurement matrix (7) does not explicitly apply. Nevertheless, the previous illustration shows that the spread spectrum technique is indeed still very effective in this analog setting. Actually, performance guarantees similar to Theorem 1 may be obtained in this setting.
Theorem 4. Let
then α is the unique minimizer of the ℓ_{1}minimization problem (2) with probability at least
Proof. The proof follows directly from Theorem 4.4 in [7]. Indeed, Theorem 4.4 applies to any matrices A_{Ω }associated to an orthonormal system (with respect to the probability measure used to draw Ω) that satisfies the socalled boundedness condition (see Section 4.1 of [7] for more details).
Let us denote
as U*U = I ∈ ℂ^{N×N }and
Note that Theorem 4.4 in [7] also ensures that our analog sensing scheme is stable relative to noise and non exact sparsity if condition (13) is satisfied. Also note that one can obtain a similar results using Theorems 1.1 and 1.2 of the very recently accepted article [51].
In view of this theorem, one can notice that the number of measurements needed for
accurate reconstructions of sparse signals is proportional to the sparsity s times the product
To illustrate this effect, Table 1 shows values of the product
Table 1. Influence of a chirp modulation on
4.4 Experiments
To confirm the theoretical predictions of the previous section, we consider the Dirac
and Fourier bases as sparsity matrices ψ. We then generate complex ssparse signals of size N = 1, 024 with s = 10. The positions of the nonzero coefficients are chosen uniformly at random in
{1,..., N}, their signs are set by generating a Steinhaus sequence, and their amplitudes follow
a uniform distribution in [0, 1]. The signals are then probed according to relation
(12) and reconstructed from different number of measurements m ∈ {s,..., N} by solving the ℓ_{1}minimization problem (2) with the SPGL1 toolbox. For each pair (m, s), we compute the probability of recovery over 100 simulations for different chirp
rate
Figure 4 shows the probability of recovery ϵ as a function of the number of measurements.
Figure 4. Probability of recovery ϵ of 10sparse signals as a function of the number measurement
m obtained with the measurement matrix (12) for two different sparsity basis: the Dirac
basis (left) and the Fourier basis (right). The continuous black curve corresponds to the probability of recovery for
First, in the case where ψ is the Dirac basis, one can notice that the number of measurements needed to reach
a probability of recovery of 1 slightly increases with the chirp rate
Second, in the case where ψ is the Fourier basis, the performance becomes much better in the presence of a chirp.
As predicted by the value in Table 1, the improvement is drastic when
Third, according to Table 1, the product
Finally, these results also suggest that the spread spectrum technique in the modified setting is almost universal in practice. Indeed, for the perfectly incoherent pair FourierDirac of sensingsparsity bases, the number of measurements needed for perfect recovery is around 100 and this number remains almost unchanged in presence of the linear chirp modulation. Furthermore, for the pair FourierFourier, the spread spectrum technique allows to reduce the number of measurements for perfect recovery close to this optimal value.
5 Conclusion
We have presented a compressed sensing strategy that consists of a wide bandwidth premodulation of the signal of interest before projection onto randomly selected vectors of an orthonormal basis. In a digital setting with a random premodulation, the technique was proved to be universal for sensing bases such as the Fourier or Hadamard bases, where it may be implemented efficiently. Our results were confirmed through a numerical analysis of the phase transition of the ℓ_{1}minimization problem for different pairs of sensing and sparsity bases.
The spread spectrum technique was also shown to be of great interest for realistic analog Fourier imaging. In applications such as radio interferometry and MRI, the originally digital random premodulation may be mimicked by an analog linear chirp. Explicit performance guarantees for the analog version of the technique with a chirp modulation were derived. It shows that recovery results are still enhanced in this setting, though universality does not strictly hold anymore. Numerical simulations have shown that the quality of reconstructed signals is drastically enhanced in this more realistic setting, also for pairs of sensingsparsity bases initially highly coherent, such as the FourierFourier pair.
Competing interests
Part of this study was funded by Merck Serono S.A.
Authors' contributions
GP carried out the theoretical study, the numerical experiments, and wrote most of the manuscript. PV and RG conceived of the study, participated in its design and coordination, and guided GP in the theoretical study. YW contributed to the setup of the technique for realistic Fourier imaging, participated in the design of the associated numerical experiments and in the writing of the manuscript. All authors discussed the results, read and approved the final manuscript.
Endnotes
^{a}perfect recovery is considered if the ℓ_{2 }norm between the original signal x and the reconstructed signal x* satisfy: x  x*_{2 }≤ 10^{3}x_{2}.
^{b}In a full generality, natural signals are not necessarily bandlimited. The spread
spectrum technique can easily be adapted to this case. The sensing model should simply
be modified to account for the fact that, if measurements are performed at frequencies
up to a band limit B, they unavoidably contain energy of the signal up to band limit
^{c}ℓ_{1}norm of the magnitude of the gradient.
Acknowledgements
This study was supported in part by the Center for Biomedical Imaging (CIBM) of the Geneva and Lausanne Universities, EPFL, and the Leenaards and LouisJeantet foundations, in part by the Swiss National Science Foundation (SNSF) under grant PP00P2123438, also by the EU FETOpen project FP7ICT225913SMALL: Sparse Models, Algorithms and Learning for LargeScale data, and by the EPFLMerck Serono Alliance award.
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