compressive Sensing

Introduction

Faithfully and efficiently constructing a signal from a coded signal is an age old scientific problem. The Shannon/Nyquist sampling theorem states that in order to reconstruct a signal from a captured signal it should be sampled at least twice as fast as the maximum signal bandwidth. However, for many practical applications, like imaging systems, the Nyquist sampling rate is very high, paving the way for alternative methods. To avoid a high sampling rate, signal compression is one such approach. In this paper summary, a method of compressive sensing is outlined. Compressive sensing is a method of capturing and representing compressible signals at a rate significantly below the Nyquist rate. This method employs nonadaptive linear projections that maintain the structure of the signal, which is then reconstructed using the optimization techniques outlined in <ref name="R1"> C. Dick, F. Harris, and M. Rice, “Synchronization in software defined radios—Carrier and timing recovery using FPGAs,” in Proc. IEEE Symp. Field-Programmable Custom Computing Machines, NapaValley, CA, pp. 195–204, Apr. 2000.</ref> <ref name="R2">J. Valls, T. Sansaloni, A. Perez-Pascual, V. Torres, and V. Almenar, “The use of CORDIC in software defined radios: A tutorial,” IEEE Commun. Mag., vol. 44, no. 9, pp. 46–50, Sept. 2006.</ref>.

In summary, in this paper we will learn the definition of compressible signal, how to construct a compressible signal and then how to reconstruct the compressed signal.

Compressible Signals

Let us define a discrete time signal [math]\displaystyle{ \ x }[/math] such that it has following properties:

• Real valued
• Finite length
• One dimensional

Then [math]\displaystyle{ \ x }[/math] is a [math]\displaystyle{ N \times 1 }[/math] column vector in [math]\displaystyle{ \mathbb{R}^n }[/math] with elements [math]\displaystyle{ x[n], \; n=1,2,....N }[/math]. To define a signal in [math]\displaystyle{ \mathbb{R}^n }[/math] we define the [math]\displaystyle{ N \times 1 }[/math] basis vectors [math]\displaystyle{ \ \psi^{N}_{i} }[/math]. To keep it simple, assume that the basis is orthonormal. Using a [math]\displaystyle{ N \times N }[/math] basis matrix [math]\displaystyle{ \,\psi = [\psi_1|\psi_2|......|\psi_N] }[/math], the signal [math]\displaystyle{ \ x }[/math] can be expressed as

[math]\displaystyle{ x=\sum_{i=1}^N s_{i} \psi_{i} \; \textrm{ or } \; x=\psi s }[/math],

where [math]\displaystyle{ \ s }[/math] is a [math]\displaystyle{ N \times 1 }[/math] column vector of weighting coefficients [math]\displaystyle{ s_i=\langle x,\psi_i \rangle=\psi^{T}_i x }[/math].
The signal is [math]\displaystyle{ \ K }[/math]-sparse if it is a linear combination of only [math]\displaystyle{ \ K }[/math] basis vectors. The signal will be compressible if the above representation has just a few large coefficients and many small coefficients. Please note that the above definition has the following inefficiencies:

• The initial number of samples may be very large even if the desired [math]\displaystyle{ \ K }[/math] is small.
• The set of all [math]\displaystyle{ \ N }[/math] transform coefficients, [math]\displaystyle{ \ s_i }[/math], must be calculated even though all but [math]\displaystyle{ \ K }[/math] of them will be discarded.
• Localization of the large coefficients must be encoded, thus introducing overhead.

The Problem Statement for the Compressive Sensing Problem

Compressive sensing directly acquires the compressed signal without going through the intermediate stage of acquiring [math]\displaystyle{ \ N }[/math] samples. In order to do this, a general linear measurement process is defined such that [math]\displaystyle{ \, M \lt N }[/math] inner products between [math]\displaystyle{ \ x }[/math] and a collection of vectors [math]\displaystyle{ \{\phi \}_{j=1}^M }[/math] are computed to give [math]\displaystyle{ y_{j}=\langle x, \phi_j \rangle }[/math].
Ordering the new measurement vectors [math]\displaystyle{ \ y_j }[/math] in a [math]\displaystyle{ M\times 1 }[/math] vector [math]\displaystyle{ \ y }[/math], and the measurement vectors [math]\displaystyle{ \phi_{j}^{T} }[/math] as rows in an [math]\displaystyle{ M \times N }[/math] matrix [math]\displaystyle{ \ \phi }[/math] we can use [math]\displaystyle{ \ x=\psi s }[/math] to get

[math]\displaystyle{ \ y=\phi x=\phi \psi s=\theta s }[/math],

where [math]\displaystyle{ \ \theta=\phi \psi }[/math] is an [math]\displaystyle{ M \times N }[/math] matrix. This measurement process is nonadaptive, which implies that [math]\displaystyle{ \ \phi }[/math] is fixed and does not depend on the signal [math]\displaystyle{ \ x }[/math]. So, in order to design a viable compressive sensing method the following two conditions must be met:

• A stable measurement matrix [math]\displaystyle{ \ \phi }[/math] must be constructed which preserves the information while reducing the dimension from [math]\displaystyle{ \ N }[/math] to [math]\displaystyle{ \ M }[/math].
• A reconstructing algorithm must be designed such that it recovers [math]\displaystyle{ \ x }[/math] from [math]\displaystyle{ \ y }[/math] by using only [math]\displaystyle{ M \approx K }[/math] measurements.

Constructing a Stable Measurement Matrix

Given the [math]\displaystyle{ \, M }[/math] available measurements, [math]\displaystyle{ \, y }[/math], where [math]\displaystyle{ \,M \lt N }[/math],[math]\displaystyle{ \,\phi }[/math] must be able to reconstruct the signal [math]\displaystyle{ \, x }[/math] with a high level of accuracy. If [math]\displaystyle{ \,x }[/math] is [math]\displaystyle{ \,K }[/math]-sparse and the location of the nonzero coefficients in [math]\displaystyle{ \,s }[/math] is known, then the problem can be solved provided [math]\displaystyle{ M \geq K }[/math]. For this problem to be well conditioned we have the following necessary and sufficient condition.

[math]\displaystyle{ 1-\epsilon \leq \frac{\parallel \theta v\parallel_2}{\parallel v\parallel_2}\leq 1+\epsilon }[/math],

where [math]\displaystyle{ \,v }[/math] is a vector sharing the same non zero entries as [math]\displaystyle{ \,s }[/math] and [math]\displaystyle{ \epsilon \geq 0 }[/math]. This means [math]\displaystyle{ \,\theta }[/math] must preserve the lengths of these particular K-sparse vectors. This condition is referred to as the restricted isometry property (RIP)<ref name="R1"> C. Dick, F. Harris, and M. Rice, “Synchronization in software defined radios—Carrier and timing recovery using FPGAs,” in Proc. IEEE Symp. Field-Programmable Custom Computing Machines, NapaValley, CA, pp. 195–204, Apr. 2000.</ref> <ref name="R2">J. Valls, T. Sansaloni, A. Perez-Pascual, V. Torres, and V. Almenar, “The use of CORDIC in software defined radios: A tutorial,” IEEE Commun. Mag., vol. 44, no. 9, pp. 46–50, Sept. 2006.</ref>. A similar property called incoherence requires that the rows [math]\displaystyle{ \,\{\phi_j\} }[/math] of [math]\displaystyle{ \,\phi }[/math] cannot sparsely represent the columns [math]\displaystyle{ \,\{\psi_i\} }[/math] of [math]\displaystyle{ \,\psi }[/math].

The RIP and incoherence conditions can be satisfied with high probability by choosing a random matrix, [math]\displaystyle{ \,\phi }[/math]. For example, let us construct a matrix [math]\displaystyle{ \,\phi_{j,i} }[/math] such that its matrix elements are independent and identically distributed (iid) random variables from a Gaussian probability distribution with mean zero and variance [math]\displaystyle{ \,1/N }[/math] <ref name="R1"/><ref name="R2"/><ref name="R4> R.G. Baraniuk, M. Davenport, R. DeVore, and M.D. Wakin, "A simples proof of the restricted isometry principle for random matrices (aka the Johnson-Linstrauss lemma meets compressed sensing)," Constructive Approximation, 2007.</ref>. Then, the measurements [math]\displaystyle{ \,y }[/math] are merely [math]\displaystyle{ \,M }[/math] different randomly weighted linear combinations of the elements of [math]\displaystyle{ \,x }[/math] and the matrix [math]\displaystyle{ \,\phi }[/math] has the following properties:

1. The incoherence property is satisfied by the matrix [math]\displaystyle{ \,\phi }[/math] with the basis [math]\displaystyle{ \,\psi = I }[/math] with high probability since it is unlikely that the rows of the Gaussian matrix [math]\displaystyle{ \,\phi }[/math] will sparsely represent the columns of the identity matrix and vice versa. In this case, if [math]\displaystyle{ \, M \geq cK\log(N/K) }[/math] with [math]\displaystyle{ \,c }[/math] a small constant, then [math]\displaystyle{ \,\theta = \phi\psi = \phi I = \phi }[/math] will have the RIP with high probability .
2. The matrix [math]\displaystyle{ \,\phi }[/math] is universal, meaning that [math]\displaystyle{ \,\theta }[/math] will satisfy the RIP property with high probability regardless of the choice for [math]\displaystyle{ \,\psi }[/math].

Reconstruction Algorithm

Now we are left with the task of designing a reconstruction algorithm. The reconstruction algorithm must take into account the [math]\displaystyle{ \,M }[/math] measurements in the vector [math]\displaystyle{ \,y }[/math], the random measurement matrix [math]\displaystyle{ \,\phi }[/math] and the basis [math]\displaystyle{ \,\psi }[/math] and then reconstruct the length [math]\displaystyle{ \,N }[/math] signal [math]\displaystyle{ \,x }[/math] or its sparse coefficient vector [math]\displaystyle{ \,s }[/math]. Since [math]\displaystyle{ \,M\lt N }[/math], the system is underdetermined, and we have infinitely many [math]\displaystyle{ \,s' }[/math] which satisfy

[math]\displaystyle{ \,\theta s'=y }[/math].

The justification for this is that if [math]\displaystyle{ \,\theta s=y }[/math] then [math]\displaystyle{ \,\theta (s+r)=y }[/math] for any vector [math]\displaystyle{ \,r }[/math] in the null space of [math]\displaystyle{ \,\theta }[/math], denoted [math]\displaystyle{ \,N(\theta) }[/math] . This suggests finding the signal's sparse coefficient vector in the [math]\displaystyle{ \,(N-M) }[/math] dimensional translated null space [math]\displaystyle{ \,H=N(\theta)+s }[/math]. The reconstruction process can be attempted in the following ways:

Minimum [math]\displaystyle{ \,l_2 }[/math] norm reconstruction

Recall that [math]\displaystyle{ (\parallel s \parallel_p)^p=\sum_{i=1}^{N} |s_i|^p }[/math]. In order to find the vector [math]\displaystyle{ \,s }[/math], the classical approach is to find the vector in the transformed null space with the smallest [math]\displaystyle{ \,l_2 }[/math] norm (also called energy norm) by solving

[math]\displaystyle{ \hat{s}=\arg\min\parallel s'\parallel_2 \; \textrm{ such } \; \textrm{ that } \; \theta s'=y. }[/math]

The closed form solution is given by [math]\displaystyle{ \hat{s}=\theta^{T}(\theta \theta^{T})^{-1}y }[/math]. However, the minimization returns non-sparse [math]\displaystyle{ \hat{s} }[/math] with many non-zero elements. In other words, it is almost never able to reconstruct the actual signal.

Minimum [math]\displaystyle{ \,l_0 }[/math] norm reconstruction

Let us now evaluate the [math]\displaystyle{ \,l_0 }[/math] norm, also known as the cardinality function, since it counts the number of non-zero entries in [math]\displaystyle{ \,s }[/math]. In this case the problem formulation is

[math]\displaystyle{ \hat{s}=\arg\min\parallel s'\parallel_0 \; \textrm{such} \; \textrm{that} \; \theta s'=y }[/math]

At first glance this approach looks attractive since it recovers the K-sparse signal exactly with high probability using only M=K+1 iid Gaussian measurements. However, solving the equation is numerically unstable and NP-complete which requires an exhaustive search of all [math]\displaystyle{ (_K^{N}) }[/math] possible locations of the non-zero entries in [math]\displaystyle{ \,s }[/math].

Minimum [math]\displaystyle{ \,l_1 }[/math] norm reconstruction

Now, if we perform minimization on the [math]\displaystyle{ \,l_1 }[/math] norm , we are able to recover K-sparse signals and closely approximate compressible signal with high probability using only [math]\displaystyle{ M \geq cK\log(\frac{N}{K}) }[/math] iid Gaussian measurements. We aim to solve

[math]\displaystyle{ \hat{s}=\arg\min\parallel s'\parallel_1 }[/math] such that [math]\displaystyle{ \,\theta s_'=y }[/math]
This optimization is convex optimization problem which reduces to linear program basis pursuit<ref name="R1"> C. Dick, F. Harris, and M. Rice, “Synchronization in software defined radios—Carrier and timing recovery using FPGAs,” in Proc. IEEE Symp. Field-Programmable Custom Computing Machines, NapaValley, CA, pp. 195–204, Apr. 2000.</ref> <ref name="R2">J. Valls, T. Sansaloni, A. Perez-Pascual, V. Torres, and V. Almenar, “The use of CORDIC in software defined radios: A tutorial,” IEEE Commun. Mag., vol. 44, no. 9, pp. 46–50, Sept. 2006.</ref>, having computational complexity [math]\displaystyle{ \,O(n^3) }[/math].

Example

The paper discusses a practical example of this technique using a "single-pixel compressive sensing camera"<ref name = "R3"> D. Takhar, V. Bansal, M. Wakin, M. Duarte, D. Baron, J. Laska, K.F. Kelly, and R.G. Baraniuk, “A compressed sensing camera: New theory and an implementation using digital micromirrors,” in Proc. Comput. Imaging IV SPIE Electronic Imaging, San Jose, Jan. 2006.</ref>. The main idea is that the camera has a mirror for each pixel and, randomly, the mirror either reflects the light to something that can use it (a photodiode) or it doesn't. Thus, [math]\displaystyle{ \,y_i }[/math] is the voltage at each photodiode and, as in the problem description, it is the inner product of the image we want, [math]\displaystyle{ \,x }[/math] and [math]\displaystyle{ \,\phi_i }[/math]. Here, [math]\displaystyle{ \,\phi_i }[/math] is a vector of ones and zeroes, indicating whether mirrors directed light towards the photodiode or not. This can be repeated to get [math]\displaystyle{ \,y_1,\dots,y_M }[/math]. The image can be reconstructed using the techniques discussed. This method requires 60% fewer measurements.

Conclusion

The compressive sensing algorithm can be applied to analog signals as well. The sensing technique finds many practical applications in image processing and similar fields. In this summary, we learned about compressive sensing which is a more efficient method compared to traditional techniques.

References

<references />