In signal processing one wants to reconstruct a signal from highly incomplete sets of linear measurements of the signal, that is, the number of measurements is much smaller than the dimension of the signal. More precisely, assuming with, one wants to reconstruct an unknown signal from a set of m measurements b = Ax₀. This requires one to solve the system of linear equations Ax = b (1)

to determine the solution that is exactly equal to x₀. Since system (1) is consistent and underdetermined, it has infinitely many solutions making it difficult to find the correct solution x₀. In many actual applications, such as image reconstruction and decoding, however, the signal one wants to reconstruct is known to be sparse (or nearly sparse) in the sense that its coefficients in some orthonormal basis are mostly zero (or approximately zero). The theory of compressed sensing [1-5] reveals that signals that have sparse representations can be reconstructed with high precision from far fewer measurements than the dimension of the signal itself. In fact, if the columns of A are chosen from a suitable distribution and the signal is sufficiently sparse, then the signal can be exactly recovered by solving the following standard l₁-norm minimization problem:

where. This optimization problem of a convex objection function can be solved effectively and it has broad applications [6-10]. But the iterative l₁-minimization method has a shortcoming in finding the sparsest solution. Since the larger entries of x in each iteration skew the l₁-norm, they are more heavily penalized in the l₁-minimization process. To address this imbalance, weighted algorithms were introduced to reduce the influence of the larger entries. Two major algorithms designed for this purpose are the reweighted l₁-minimization and l₁ greedy algorithms [11,12].

Suppose that is the sequence of vectors generated by l₁-minimization. In the k-th iteration of the reweighted l₁-minimization method [11], one minimizes instead of in (2), where

Observe that the weights in (3) are roughly inversely proportional to the sizes of the entries of the previous iterate x^k−1. So the larger entries are weighted down to rectify their undue influence in the next iteration of the l₁-minimization process. Numerical experiments [11] have indicated that the reweighted l₁-minimization recovers random sparse signals with a much higher probability than the standard l₁-minimization in (2). The reweighted l₁-minimization algorithm has been extensively studied in recent years. The l_q-minimization problem, 0 < q ≤ 1, was discussed and implemented using the reweighted l₁-minimization scheme [13,14]. A two-step reweighted l₁-minimization was introduced to improve the recovery of sparse signals [15], and a reweighted l₁- minimization for a nonuniform sparsity model was proposed [16]. The performance of the reweighted l₁-minimization with noisy data was also rigorously analyzed [17], and some convergence conditions of reweighted l₁-minimization for a special family of measurement matrices were studied [18].

In the l₁ greedy algorithm [12], instead of using variable weights as in (3) the weights are set to a fixed small constant for entries whose magnitude is above a certain threshold and to 1 for the other entries. This threshold is lowered after each iteration so that more and more large entries are weighted down by in each subsequent iteration step. More precisely, the weights in the k-th iteration of the l₁ greedy algorithm are defined by

where, x⁰ is generated by the standard l₁-minimization, and. Numerical experiments showed that the l₁ greedy algorithm outperforms both the unweighted and reweighted l₁-minimization algorithms in recovering random sparse signals [12,19].

A generalized l₁ greedy algorithm in the compressed sensing framework was recently introduced by the authors of [20]. The new algorithm not only incorporates the threshold feature of the l₁ greedy algorithm to counteract the influence of large entries but also assigns significantly large weights to the smallest nonzero entries to speed up the identification of nonzero entries. Moreover, in contrast to the l₁ greedy algorithm where the remaining entries are assigned a neutral weight, the remaining entries in the generalized l₁ greedy algorithm receive weight roughly inversely proportional to their magnitudes as in the reweighted l₁-minimization algorithm. Thus the generalized l₁ greedy algorithm not only incorporates features of both the l₁ greedy and reweighted l₁-minimization algorithms but also enhances the impact of the small entries in the l₁-minimization process.

Generalized l₁ greedy algorithm

1) Generate x⁰ by the reweighted l₁-minimization;

2) For k = 1 to k_max;

a) Update the weight matrix W^k; where

b) Solve weighted l₁-minimization problem:

c) Return if a stopping criterion is met.

In [20] the generalized l₁ greedy algorithm was applied to the problem of reconstructing essentially piecewise constant medical images in computerized tomography (CT) in the compressed sensing framework via total variation minimization. Tested with the Shepp-Logan phantom and a real cardiac CT image, the generalized l₁ greedy algorithm was shown to perform better than the reweighted l₁-minimization and l₁ greedy algorithms. In particular, it was observed that in the context of reconstructing these two images the generalized l₁ greedy algorithm was superior to the others at distinguishing small gradients. However, to show that the generalized l₁ greedy algorithm is truly superior to the other two algorithms at detecting small entries in general we should compare the performance of the three algorithms in recovering random sparse signals. So in this paper, following [11,12], we present a series of rigorous numerical studies of the performance of the generalized l₁ greedy algorithm in the general setting of Gaussian random matrices A and random sparse signals x. The rest of this paper is organized as follows. In Section 2, the relative frequencies of successful recovery of random Gaussian sparse signals for the reweighted l₁-minimization, l₁ greedy, and generalized l₁ greedy algorithms are compared. Section 3 presents the stability of the generalized l₁ greedy algorithm with respect to its parameters. Section 4 studies the performance of the generalized l₁ greedy algorithm on noisy data. Section 5 shows that the generalized l₁ greedy algorithm is better at detecting smaller entries in the general setting than the other two algorithms. Section 6 concludes with a brief summary of the generalized l₁ greedy algorithm and the results.

In our first experiment we want to determine how well each of the three algorithms can recover random Gaussian sparse signals from random Gaussian measurements b = Ax. Following the same approach taken in [11], we implement each of the three algorithms in MATLAB and invoke the l1eq-pd solver from the l₁-MAGIC software package developed by E. Candes and J. Romberg (available at www.l1-magic.org). We set m = 128 and n = 256. For each trial, a random matrix with i.i.d. Gaussian entries is selected and its columns are normalized. A random k-sparse signal is also selected in such a way that the k nonzero positions are randomly distributed and the nonzero components satisfy the standard Gaussian distribution. We run 150 trials for each sparsity level k between 50 and 90. The total number of iterations (excluding the initial l₁-minimization step) for each of the three algorithms is set to 16. For the generalized l₁ greedy algorithm we start with 4 iterations of the reweighted l₁-minimization. The criterion for successful recovery for all three algorithms is set to

where x is the reconstruction of x₀ by the algorithm. The parameters chosen for the three algorithms are listed as follows:

1) In the reweighted l₁-minimization:.

2) In the l₁ greedy algorithm:;; others are the same as in 1).

3) In the generalized l₁ greedy algorithm:; s = 0.8;; others are the same as in 2).

The settings in this section will be used throughout the paper unless changes are explicitly stated otherwise.

The output of this experiment is presented in Figure 1. As one can see from the graph, for a fixed sparsity level k the probability of successful recovery of a k-sparse signal by the generalized l₁ greedy algorithm is higher than in the both cases of the reweighted l₁-minimization and l1 greedy algorithms. On average, the l₁ greedy algorithm and the generalized l₁ greedy algorithm recover about 14% and 18% more entries than the reweighted l₁-minimization method, respectively, for 50 ≤ k ≤ 90. Furthermore, on average, the generalized l₁ greedy algorithm recovers about 6% more entries than the l₁ greedy algorithm.

An empirical analysis of the reweighted l₁-minimization

algorithm determined that the algorithm is robust with respect to (chosen from a suitable range) and that much of the improvement in recovery comes from the first few reweighting iterations [11]. We performed a similar analysis of the generalized l₁ greedy algorithm, and our results indicate that the algorithm is stable with respect to each of its parameters (within a certain range of values). We illustrate this behavior with the parameter using the same settings as in Section 2 for the remaining parameters. From Figure 2 one can see that the algorithm is fairly stable for the following values of: 0.2; 0.3; 0.4. Our experimental results also show that the algorithm is very robust with respect to for and fairly robust with respect to s and for values between 0.7 and 0.9. It is also evident from Figure 3 that the number of iterations k_max of the generalized l₁ greedy algorithm has minimal affect on the performance of the algorithm when k_max ≥ 10. So in practice only a few iterations are needed to achieve the best performance of the generalized l₁ greedy algorithm.

In real life applications measured data are often corrupted by a small amount of noise. Thus one needs to recover the original signal x₀ from noisy data

;

where is an unknown noise term. The signal-tonoise ratio (SNR) in dB is defined by

where b = Ax₀ is noise-free data. In this section we show how white Gaussian noise at SNR levels 40 dB and 60 dB, respectively, affect the performance of the generalized l₁ greedy algorithm. We also compare the performance of the reweighted l₁-minimization, l₁ greedy, and generalized l₁ greedy algorithms on noisy data with an SNR of 60 dB. As in [12] the precision of recovery is set according to the noise level. More precisely, the criterion for successful recovery are taken to be and for noisy data with an SNR of 40 dB and with an SNR of 60 dB, respectively. All the other settings are the same as in Section 2. Figure 4 shows that the performance of the generalized l₁ greedy algorithm is very robust with respect to noise at an SNR level 60 dB and fairly robust with respect to noise at an SNR level 40 dB for 30 ≤ k ≤ 90. Figure 5 compares the performance of the three algorithms on noisy data with an SNR of 60 dB. Clearly, the generalized l₁ greedy algorithms outperform the other two algorithms. Moreover, for noisy data with an SNR of 60 dB, on average, the generalized l₁ greedy algorithm recovers about 17% more entries than the reweighted l₁-minimization algorithm and about 5% more entries than the l₁ greedy algorithm for 50 ≤ k ≤ 90.

It is known that the l₁ greedy algorithm outperforms the reweighted l₁ minimization algorithm in finding spare signals [12,19]. However, the reweighted l₁-minimization algorithm was designed to help speed up the detection of small entries [11]. The generalized l₁ greedy algorithm of [20], which incorporates features of both algorithms, should have the performance advantage of the l₁ greedy algorithm while enhancing the power of the reweighted l₁-minimization algorithm in detecting small entries. In fact, the generalized l₁ greedy algorithm appears to be superior to the other two algorithms in distinguishing small gradients in the task of reconstructing images via total variation minimization [20]. In this section we want to see how well the generalized l₁ greedy algorithm would perform in recuperating random sparse signals with a guaranteed percentage of small entries. More precisely, in our last experiment we want to determine the extent to which the ratio of very small entries in the sparse signals affects the probability of successful recovery by each of the algorithms under consideration. The entries of the sparse signal in each trial are obtained from a mixed Gaussian distribution as follows: a random 30% of the entries are generated using a Gaussian distribution with mean 0 and standard deviation 0.01 while the remaining 70% of the entries are generated using the standard Gaussian distribution. We need to fine tune the generalized l₁ greedy algorithm to make it most efficient at detecting the small entries in the range we set. Experimental trials show that setting results in the best performance. The values of the other parameters are left unchanged. We then run 150 trials for each sparsity level k, 50 ≤ k ≤ 105, and set the criterion for successful recovery to. As one can see from Figure 6" target="_self"> Figure 6, the generalized l₁ greedy algorithm vastly outperforms both the reweighted l₁-minimization and the l₁ greedy algorithms in recovering sparse solutions containing a few nonzero small entries. Moreover, on average, the generalized l₁ greedy algorithm recovers 32% more entries than the reweighted l₁-minimization algorithm and 11% more entries than the l₁ greedy algorithm for 50 ≤ k ≤ 105.

Our statistical experiments indicate that the generalized l₁ greedy algorithm outperforms the reweighted l₁-minimization and l₁ greedy algorithms in recovering random sparse signals from random Gaussian measurements. In fact, the generalized l₁ greedy algorithm recovers more entries than the other two algorithms. Moreover, the performance of the algorithm is robust with respect to its parameters and to noisy data at different noise levels. Finally, the generalized l₁ greedy algorithm performs

extremely well in detecting small entries of unknown sparse signals thereby dramatically speeding up their recovery via l₁-minimization. It is expected that more details of signals could be recovered by using the generalized l₁ greedy algorithm without extra cost.

F. Arroyo was supported by a faculty research grant from Francis Marion University. X. Li and J. Zhu were partially supported by a Faculty Research Award and a COSM Interdisciplinary Pilot Fund from Georgia Southern University.