Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]

There is an extensive body of literature on image compression, but the central concept is straightforward: we transform the image into an appropriate basis and then code only the important expansion coefficients. The crux is finding a good transform, a problem that has been studied extensively from both a theoretical [14] and practical [25] standpoint. The most notable product of this research is the wavelet transform [9], [16]; switching from sinusoid-based representations to wavelets marked a watershed in image compression and is the essential difference between the classical JPEG [18] and modern JPEG-2000 [22] standards. Image compression algorithms convert high-resolution images into a relatively small bit streams (while keeping the essential features intact), in effect turning a large digital data set into a substantially smaller one. But is there a way to avoid the large digital data set to begin with? Is there a way we can build the data compression directly into the acquisition? The answer is yes, and is what compressive sampling (CS) is all about

Turan's method and compressive sampling

Turan's method, as expressed in his books, is a careful study of trigonometric polynomials from different points of view. The present article starts from a problem asked by Turan: how to construct a sequence of real numbers x(j) (j= 1,2,...n) such that the almost periodic polynomial whose frequencies are the x(j) and the coefficients are 1 are small (say, their absolute values are less than n d, d< given) for all integral values of the variable m between 1 and M= M(n,d) ? The best known answer is a random choice of the x(j) modulo 1. Using the random choice as Turan (and before him Erd\"os and Renyi), we improve the estimate of M (n, d) and we discuss an explicit construction derived from another chapter of Turan's book. The main part of the paper deals with the corresponding problem when R / Z is replaced by Z / NZ, N prime, and m takes all integral values modulo 1 except 0. Then it has an interpretation in signal theory, when a signal is representad by a function on the cyclic goup G = Z / NZ and the frequencies by the dual cyclic group G^ : knowing that the signal is carried by T points, to evaluate the probability that a random choice of a set W of frequencies allows to recover the signal x from the restriction of its Fourier tranform to W by the process of minimal extrapolation in the Wiener algebra of G^(process of Cand\`es, Romberg and Tao). Some random choices were considered in the original article of CRT and the corresponding probabilities were estimated in two preceding papers of mine. Here we have another random choice, associated with occupancy problems

Random Filters for Compressive Sampling

This paper discusses random filtering, a recently proposed method for directly acquiring a compressed version of a digital signal. The technique is based on convolution of the signal with a fixed FIR filter having random taps, followed by downsampling. Experiments show that random filtering is effective at acquiring sparse and compressible signals. This process has the potential for implementation in analog hardware, and so it may have a role to play in new types of analog/digital converters

Compressive Sampling for Remote Control Systems

In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained L2 optimization of the control performance, which is reducible to L1-L2 optimization. The low rate random sampling employed in the proposed method based on the compressive sampling, in addition to the fact that the L1-L2 optimization can be effectively solved by a fast iteration method, enables us to generate the sparse control signal with reduced computational complexity, which is preferable in remote control systems where computation delays seriously degrade the performance. We give a theoretical result for control performance analysis based on the notion of restricted isometry property (RIP). An example is shown to illustrate the effectiveness of the proposed approach via numerical experiments