## Abstract

A signature result in compressed sensing is that Gaussian random sampling achieves stable and robust recovery of sparse vectors under optimal conditions on the number of measurements. However, in the context of image reconstruction, it has been extensively documented that sampling strategies based on Fourier measurements outperform this purportedly optimal approach. Motivated by this seeming paradox, we investigate the problem of optimal sampling for compressed sensing. Rigorously combining the theories of wavelet approximation and infinite-dimensional compressed sensing, our analysis leads to new error bounds in terms of the total number of measurements *m* for the approximation of piecewise \(\alpha \)-Hölder functions. Our theoretical findings suggest that Fourier sampling outperforms random Gaussian sampling when the Hölder exponent \(\alpha \) is large enough. Moreover, we establish a provably optimal sampling strategy. This work is an important first step towards the resolution of the claimed paradox and provides a clear theoretical justification for the practical success of compressed sensing techniques in imaging problems.

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## Notes

In particular, by ‘Fourier measurements’ we mean samples of the continuous Fourier transform of

*f*, not its discrete Fourier transform. Not only is this more convenient for the analysis, it is also more relevant in practice, since modalities such as MRI are based on the continuous Fourier transform [10].The entries of the cross-Gramian matrix

*U*(8.1) used in this sampling strategy are computed by applying the inverse discrete wavelet and Fourier transforms to the first*N*elements of the canonical basis of the augmented space \({\mathbb {R}}^{16N}\). Then, only the*N*entries corresponding to the frequencies of interest are kept. This augmentation makes the computation of*U*more accurate.In order to avoid discretization effects related to the wavelet crime, the vector

*d*of wavelet coefficients is computed by sampling the function*f*on a uniform grid of 16*N*points, applying the discrete wavelet transform and then keeping the first*N*of entries of the resulting vector.

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## Acknowledgements

The authors extend their thanks to Vegard Antun (University of Oslo), who performed the experiment in Fig. 1. They also would like to thank Anders C. Hansen, Bradley J. Lucier and Clarice Poon. S.B. acknowledges the support of the PIMS Postdoctoral Training Centre in Stochastics, the Department of Mathematics of Simon Fraser University, NSERC, the Faculty of Arts and Science of Concordia University, and the CRM Applied Math Lab. This work was supported by the PIMS CRG in “High-dimensional Data Analysis” and by NSERC through Grant R611675.

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Communicated by Albert Cohen.

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## Appendices

### Fourier Transform and Series

Given \(f \in L^1({\mathbb {R}}) \cap L^2({\mathbb {R}})\), we define the Fourier transform as

If \(f \in L^2([0,1])\), then we can write *f* as its Fourier series

where

is the Fourier basis for \(L^2([0,1])\). If we consider *f* as a function in \(L^2({\mathbb {R}})\) that is zero outside [0, 1], then \(\langle f, \gamma _n \rangle _{L^2} = {\hat{f}}(2 \pi n)\). For convenience, we also re-index this basis over \({\mathbb {N}}\) as follows:

### Orthogonal Wavelet Bases of \(L^2([0,1])\)

Let \(\varphi \) and \(\psi \) be the scaling function and mother wavelet, respectively, of the Daubechies’ wavelet with \(p \ge 1\) vanishing moments. Write

Since we work with functions on the interval [0, 1], we need an orthonormal wavelet basis of \(L^2([0,1])\). We construct this via periodization (see (5.1) and [53, Sect. 7.5.1] for more details). Define the coarsest scale

(In general, one could allow any fixed \(j_0\) greater than or equal to the right-hand side. However, this does not affect any of the results in the paper; hence, we simply specify \(j_0\) exactly.) We recall that Daubechies’ wavelets with *p* vanishing moments have the smallest possible support, of length \(2p-1\). We assume the scaling function \(\varphi \) and the mother wavelet \(\psi \) to be supported on \([0, 2p-1]\) and \([-p+1,p]\), respectively. Then, the set of functions

is an orthonormal basis of \(L^2([0,1])\), referred to as the *periodized Daubechies wavelet basis*. We note in passing that

that is, wavelets that are fully supported in [0, 1] are unchanged, and

where the functions in the right-hand sides are implicitly restricted to [0, 1]. As needed, we order the basis (B.2) in the usual way, rewriting it as \(\{ \phi _n \}_{n \in {\mathbb {N}}}\), where

### Proof of Theorem 9.1

The technical tools we need to prove this theorem were introduced in [1], where a similar result was proven for the weighted quadratically constrained basis pursuit decoder.

We require several concepts from [1]. First, we introduce several additional pieces of notation. Given sparsity levels \({\mathbf {M}} = (M_1,\ldots ,M_r)\) and local sparsities \({\mathbf {s}} = (s_1,\ldots ,s_r)\), let

be the set of all possible supports of an \(({\mathbf {s}},{\mathbf {M}})\)-sparse vector. Given positive weights \(w = (w_i)^{M}_{i=1} \in {\mathbb {C}}^{M}\) and a set \({\varDelta }\subseteq \{1,\ldots ,M\}\), we define its weighted cardinality as follows:

The conventional tool in compressed sensing for establishing recovery guarantees is the so-called Restricted Isometry Property (RIP). In our case, we require an generalized version of the RIP. This takes into account the sparsity in levels structure, and the fact that the measurement matrix *A* satisfies (9.3), rather than the more standard condition \({\mathbb {E}}(A^*A) = I\).

### Definition 9

*(G-adjusted RIP in Levels)* Let \({\mathbf {M}} = (M_1,\ldots ,M_r)\) be sparsity levels, \({\mathbf {s}} = (s_1,\ldots ,s_r)\) be local sparsities and \(G \in {\mathbb {C}}^{M \times M}\) be invertible, where \(M = M_r\) is the sparsity bandwidth. The \(({\mathbf {s}},{\mathbf {M}})\)th *G**-adjusted Restricted Isometry Constant in Levels (G-RICL)* \(\delta _{{\mathbf {s}},{\mathbf {M}},G}\) of a matrix \(A \in {\mathbb {C}}^{m \times M}\) is the smallest \(\delta \ge 0\) such that

If \(0< \delta _{{\mathbf {s}},{\mathbf {M}},G} < 1\), then the matrix is said to have the *G**-adjusted Restricted Isometry Property in levels (G-RIPL)* of order \(({\mathbf {s}},{\mathbf {M}})\).

In our setting, if *N*, *M* are such that \(P_N UP_M\) is full rank (in particular, if the balancing property holds), then *G* will be taken as the unique positive definite square-root of the positive definite matrix \(P_M U^* P_N U P_M\). We write \(G = \sqrt{P_M U^* P_N U P_M}\) in this case.

The following result [1, Thm. 3.6] gives conditions under which the matrix *A* satisfies the G-RIPL:

### Theorem C.1

Let \(0< \delta ,\varepsilon <1\), \(M \ge 2\), \(1 \le {\tilde{r}} \le r \le N\) and \({\mathbf {M}} = (M_1,\ldots ,M_r)\) and \({\mathbf {s}} = (s_1,\ldots ,s_r)\) be sparsity levels and local sparsities, respectively, where \(s = s_1+\cdots +s_r \ge 2\) and \(M_r = M\). Let \({\varOmega }\) be an \(({\mathbf {N}},{\mathbf {m}})\)-multilevel random subsampling pattern with *r* levels and saturation \({\tilde{r}}\), and \(N = N_r\). Suppose that *N*, *M* are such that \(P_N U P_M\) is full rank, where *U* is as in (8.1) and consider the matrix *A* given by (8.4). If

where

then, with probability at least \(1-\varepsilon \), *A* satisfies the G-RIPL of order \(({\mathbf {s}},{\mathbf {M}})\) with constant \(\delta _{{\mathbf {s}},{\mathbf {M}},G} \le \delta \) and *G* given by \(G = \sqrt{P_M U^* P_N U P_M}\).

In order to establish Theorem 9.1, we next show that the G-RIPL implies stable and robust recovery. To do so, we first introduce the following generalization of the so-called robust Null Space Property (rNSP):

### Definition 10

Let \({\mathbf {M}} = (M_1,\ldots ,M_r)\) be sparsity levels, \({\mathbf {s}} = (s_1,\ldots ,s_r)\) be local sparsities and \(w \in {\mathbb {C}}^{M}\) be positive weights, where \(M = M_r\). A matrix \(A \in {\mathbb {C}}^{m \times M}\) has the *weighted robust null space property in levels (weighted rNSPL)* of order \(({\mathbf {s}},{\mathbf {M}})\) with constants \(0< \rho < 1\) and \(\gamma > 0\) if

for all \(x \in {\mathbb {C}}^M\) and \({\varDelta }\in D_{{\mathbf {s}},{\mathbf {M}}}\).

Suppose the weights \(w = (w_i)^{M}_{i=1}\) are of the form (9.8), i.e. constant on the sparsity levels, and define

The following combines Lemmas 5.2 and 5.3 of [1]:

### Lemma 6

Suppose that *A* has the weighted rNSPL of order \(({\mathbf {s}},{\mathbf {M}})\) with constants \(0< \rho < 1\) and \(\gamma > 0\). Let \(x,z \in {\mathbb {C}}^M\). Then,

and

The G-RIPL implies the weighted rNSPL (see [1, Thm. 5.5]):

### Theorem C.2

Let \(A \in {\mathbb {C}}^{m \times M}\) and \(G \in {\mathbb {C}}^{M \times M}\) be invertible. Let \({\mathbf {M}} = (M_1,\ldots ,M_r)\) and \({\mathbf {s}} = (s_1,\ldots ,s_r)\) be sparsity levels and local sparsities, respectively, and \({\mathbf {w}}\) be positive weights of the form (9.8). Let \(0< \rho < 1\), and suppose that *A* has the G-RIPL of order \(({\mathbf {t}},{\mathbf {M}})\) and constant 1/2, where \({\mathbf {t}} = (t_1,\ldots ,t_r)\) satisfies

and \(\kappa (G)=\Vert G\Vert _{\ell ^2}\Vert G^{-1}\Vert _{\ell ^2}\) is the condition number of *G* with respect to the \(\ell ^2\)-norm. Then, there exists \(0 < \gamma \le \sqrt{2} {\left\| G^{-1}\right\| }_{\ell ^2}\) such that *A* has the weighted rNSPL of order \(({\mathbf {s}},{\mathbf {M}})\) with constants \(\rho \) and \(\gamma \).

Finally, we are now ready to prove Theorem 9.1:

### Proof of Theorem 9.1

Recall that \(G^2 = P_M U^* P_N U P_M\). Hence, *G* is invertible since *U* has the balancing property (9.4), and moreover, we have

We also have \({\left\| G\right\| }_{\ell ^2} \le 1\) since *U* is unitary, and therefore, \(\kappa (G) \le 1/\sqrt{\theta }\).

Let \(t_l\) be given by (C.2) with \(\rho = 1/2\). Recalling (9.9) and (C.1), observe that

Therefore,

and

Hence, condition (C) and Theorem C.1 imply that the matrix *A* has the G-RIPL of order \(({\mathbf {t}},{\mathbf {M}})\) with constant \(\delta _{{\mathbf {t}},{\mathbf {M}},G} \le 1/2\). It now follows from Theorem C.2 that *A* has the weighted rNSPL of order \(({\mathbf {s}},{\mathbf {M}})\) with constants \(\rho = 1/2\) and \(\gamma \le \sqrt{2} {\Vert G^{-1}\Vert }_{\ell ^2} \le \sqrt{2/\theta }\).

To complete the proof, we use Lemma 6 with \(z = {\hat{x}}\). Using this, (C.3) and the bounds

we see that

We now use the fact that \({\hat{x}}\) is a minimizer, and therefore,

Writing \({\left\| A ({\hat{x}} - x) \right\| }_{\ell ^2} \le {\left\| A {\hat{x}} - y\right\| }_{\ell ^2} + {\left\| A x - y\right\| }_{\ell ^2}\) and combining with the previous inequality now yields

The result now follows from the bound (D) on \(\lambda \) and the fact that \(e = y - A x\). \(\square \)

### Proofs of Lemmas 2, 3 and 4

### Proof of Lemma 2

We first observe that \(\theta = \inf _{|\omega | \le \pi } | {\hat{\varphi }}(\omega ) |^2 > 0\) for the Daubechies wavelet basis [7, Remark 7.1]. Now let \(x = (x_n)^{N}_{n=1} \in {\mathbb {C}}^N\) with \({\left\| x\right\| }_{\ell ^2} = 1\) and write \(g = \sum ^{N}_{n=1} x_n \phi _n\) for the corresponding finite wavelet expansion. Observe that \( {\left\| g\right\| }^2_{L^2([0,1])} = {\left\| x\right\| }^2_{\ell ^2} = 1\). Let \(V^{{\mathrm {per}}}_{j} = {\mathrm {span}}\{ \varphi _{j,n} : n = 0,\ldots ,2^j-1 \}\) and \(W^{{\mathrm {per}}}_{j} = {\mathrm {span}}\{ \psi _{j,n} : n = 0,\ldots ,2^j-1 \}\). Then,

and conversely every \(g \in V^{{\mathrm {per}}}_{j_0+r}\) with \( {\left\| g\right\| }^2_{L^2([0,1])} = 1\) is equivalent to a vector of coefficients \(x \in {\mathbb {C}}^M\) with \({\left\| x\right\| }_{\ell ^2} = 1\). Note also that

Hence,

Fix a \(g \in V^{{\mathrm {per}}}_{j_0+r}\) with \({\left\| g\right\| }_{L^2([0,1])} = 1\) and write

where \( {\left\| z\right\| }_{\ell ^2} = {\left\| g\right\| }_{L^2(0,1)} = 1\) and \(z = (z_k)^{N-1}_{k=0}\). Then, for any integer *n*,

where \(G(x) = \sum ^{N-1}_{k=0} z_k {\mathrm {e}}^{-2 \pi {\mathrm {i}}k x}\) is a 1-periodic function. In the first equality, we have used that

and that \(N = 2^{j_0+r}\). Hence,

Using the fact that *G* is 1-periodic, we deduce that

Now, since *G* is a trigonometric polynomial, it follows that

Therefore,

Since *g* was arbitrary, we deduce that

To complete the proof, we first recall that \(P_N - P_N U^* P_N U P_N\) is positive semidefinite (since *U* is unitary), and therefore,

as required. \(\square \)

For Lemma 3, we first require the following:

### Lemma 7

The (*k*, *l*)th local coherence satisfies

and

### Proof

By definition,

and

Recall that \(|B_k| \le 2^{j_0+k}\). Moreover, recall relation (D.2) and note that an analogous formula holds for \(\widehat{\psi _{j,k}^{{\mathrm {per}}}}\). Since \(B_k\) is a set of integers, the result now follows immediately. \(\square \)

### Proof of Lemma 3

By the previous lemma, it suffices to estimate the Fourier transform of the wavelet and scaling function in different regions of frequency space. First, suppose that \(k \ge l \ge 1\). Then, \(| \omega | \ge 2^{j_0+k-1}\) for \(\omega \in B_k\), and the smoothness conditions (2.1) give

The first estimate now follows from Lemma 7.

For the second estimate, we need to bound \(| {\hat{\psi }}(2 \pi \omega ) |\) for \(| \omega | \ll 1\). For this, we recall that \({\hat{\psi }}(z) = (-{\mathrm {i}}z)^{p} \chi _p(z)\) for some bounded function \(\chi _p(z)\) [53, Thm. 7.4]. Hence,

If \(l > k \ge 1\), then this and the previous lemma give

The result now follows immediately. \(\square \)

### Proof of Lemma 4

By direct calculation

where \(u_i = U^* e_i\) is the *i*th row of *U*. Observe that

Hence,

which gives

Since \(M = M_r\), we now apply Lemma 3 to get

Hence,

The result now follows. \(\square \)

### Numerical Experiments

In this section, we discuss some technical details behind Fig. 2. Moreover, we provide further numerical evidence to support the comparison shown therein. We consider the function

This function has *K* discontinuities in (0, 1) and its plot is shown in Fig. 4. We approximate \(f_K\) for \(K = 1,10,20\) using the four different encoder–decoder pairs described below.

**(Fourier**, \(\ell ^1\)**):** This strategy corresponds to the setting of Theorems 3.3 and 4.6 and to the error bound (1.5), up to a few minor technical modifications. The Fourier sampling strategy is as follows. We divide the frequency space into dyadic bands and consider a sampling scheme analogous to the \(({\mathbf {N}},{\mathbf {m}})\)-multilevel random subsampling strategy with saturation \({\tilde{r}}\) described in Definition 5, where symmetry of the samples is enforced in every frequency band. In particular, \({\mathbf {N}}\) is defined as in (8.5), the saturation level is \({\tilde{r}} = \text {round}(\log _2(m/2))\), and the local numbers of measurements are

where, in the last frequency band, we let \(m_r = m-(m_1 +\cdots + m_{r-1})\) in order to reach a total budget of *m* measurements exactly. The samples are then computed as follows. The first \({\tilde{r}}\) dyadic bands are saturated. For every \(k > {\tilde{r}}\), we pick \(m_k/2\) samples uniformly at random from the *k*-th frequency semiband (corresponding to positive frequencies) and we choose frequencies in the opposite semiband (corresponding to negative frequencies) in a symmetric way. The wavelet coefficients of *f* are recovered via basis pursuit (1.2). Numerically, (1.2) is solved using the MATLAB package SPGL1 (see [66, 67]) with parameters \(\texttt {bpTol}\) = 1e-6, \(\texttt {optTol}\)= 1e-6, and a maximum of 10000 iterations.^{Footnote 4}

**(Fourier**, \(\ell ^1_w\)**):** This strategy is almost identical to (Fourier, \(\ell ^1\)). The only difference is that wavelet coefficients are recovered via weighted (as opposed to unweighted) basis pursuit, i.e. by solving (1.2) where the \(\ell ^1\)-norm is replaced with the weighted \(\ell ^1_w\)-norm. The weights *w* are set according to the recipe described in Sect. 10.2 with \(\delta = 10^{-5}\). Weighted basis pursuit is numerically solved using the MATLAB package SPGL1 as in the previous case.

**(Gauss**, \(\ell ^1\)**):** This is the standard encoder–decoder pair of compressed sensing with random Gaussian measurements, corresponding to the setting of Theorems 3.1 and 4.3 and to the error bound (1.3). The vector \(d \in {\mathbb {R}}^N\) of wavelet coefficients of *f* is explicitly computed and then encoded as \(y = A d\), where \(A \in {\mathbb {R}}^{m \times N}\) has i.i.d. entries drawn from the normal distribution with mean zero and variance 1/*m*. The function is recovered by means of the basis pursuit decoder (1.2), numerically solved via SPGL1 as in the previous cases.^{Footnote 5}

**(Optimal**, \(\ell ^1\)**):** This strategy corresponds to the setting of Theorems 3.1 and 4.3 and to the optimal error bound (1.3). As in the previous case, we compute the vector \(d\in {\mathbb {R}}^N\) of wavelet coefficients of *f*. Then, the first \(m_1 = \text {round}(m/2)\) entries of *d* are directly encoded into \(y^{(1)} \in {\mathbb {R}}^{m_1}\). The remaining \(m_2 = m - m_1\) measurements are computed as \(y^{(2)} = A (d_n)_{n = m_1+1}^{N}\), where \(A \in {\mathbb {R}}^{m_2 \times (N-m_1)}\) has i.i.d. entries drawn from the normal distribution with mean zero and variance \(1/m_2\). We consider the basis pursuit decoder (1.2), numerically solved using SPGL1 as in the previous cases.

**(Gauss, Tree):** This encoder–decoder pair corresponds to the model-based compressive sensing strategy proposed in [12]. The encoder identical to (Gauss, \(\ell ^1\)), and the decoder explicitly promotes tree-structured sparsity in the recovered function using the model-based CoSaMP algorithm [12]. This strategy requires tuning a parameter *c*, which links *m* to the desired tree-sparsity level *s* as \(m = c s\). In the numerical tests, we consider \(c = 3,4,5,6,7\). We employ the Model-based Compressive Sensing Toolbox v1.1 provided by the authors of [12]. The maximum number of iterations for the outer loop of CoSaMP is set to 100.

These four encoder–decoder pairs are compared with \(N = 2^{15} = 32768\) and values of *m* ranging from \(2^3 = 8\) to \(2^{11} = 2048\). We employ Haar and db4 wavelets, having \(p= 1\) and \(p = 2\) vanishing moments, respectively. In this setting, the weights used in (Fourier, \(\ell ^1_w\)) are constant for all \(m \le 256\). The relative \(L^2\) error is computed using the wavelet coefficients of *f*, approximated as in the strategies (Gauss, \(\ell ^1\)), (Optimal, \(\ell ^1\)) and (Gauss, Tree).

In Fig. 5 the encoder–decoder pairs (Fourier, \(\ell ^1\)) and (Fourier, \(\ell ^1_w\)) have almost identical performances and they consistently outperform all the other strategies, with only a few exceptions. Moreover, this behaviour is independent of the number of discontinuities *K*. It is remarkable that (Fourier, \(\ell ^1\)) and (Fourier, \(\ell ^1_w\)) are able to numerically outperform even the theoretically optimal pair (Optimal, \(\ell ^1\)). Although our theory prescribes the use of weighted square-root LASSO decoder in the Fourier case, the numerics show that employing (weighted or unweighted) basis pursuit (1.2) is enough to numerically outperform the other strategies.

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Adcock, B., Brugiapaglia, S. & King–Roskamp, M. Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing.
*Found Comput Math* **22**, 99–159 (2022). https://doi.org/10.1007/s10208-021-09501-3

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DOI: https://doi.org/10.1007/s10208-021-09501-3

### Keywords

- Compressed sensing
- Optimal sampling strategies
- Fourier sampling
- Wavelet approximation theory
- Piecewise \(\alpha \)-Hölder functions

### Mathematics Subject Classification

- 94A20
- 94A08
- 42C40