274 research outputs found
Two are better than one: Fundamental parameters of frame coherence
This paper investigates two parameters that measure the coherence of a frame:
worst-case and average coherence. We first use worst-case and average coherence
to derive near-optimal probabilistic guarantees on both sparse signal detection
and reconstruction in the presence of noise. Next, we provide a catalog of
nearly tight frames with small worst-case and average coherence. Later, we find
a new lower bound on worst-case coherence; we compare it to the Welch bound and
use it to interpret recently reported signal reconstruction results. Finally,
we give an algorithm that transforms frames in a way that decreases average
coherence without changing the spectral norm or worst-case coherence
Frame Coherence and Sparse Signal Processing
The sparse signal processing literature often uses random sensing matrices to
obtain performance guarantees. Unfortunately, in the real world, sensing
matrices do not always come from random processes. It is therefore desirable to
evaluate whether an arbitrary matrix, or frame, is suitable for sensing sparse
signals. To this end, the present paper investigates two parameters that
measure the coherence of a frame: worst-case and average coherence. We first
provide several examples of frames that have small spectral norm, worst-case
coherence, and average coherence. Next, we present a new lower bound on
worst-case coherence and compare it to the Welch bound. Later, we propose an
algorithm that decreases the average coherence of a frame without changing its
spectral norm or worst-case coherence. Finally, we use worst-case and average
coherence, as opposed to the Restricted Isometry Property, to garner
near-optimal probabilistic guarantees on both sparse signal detection and
reconstruction in the presence of noise. This contrasts with recent results
that only guarantee noiseless signal recovery from arbitrary frames, and which
further assume independence across the nonzero entries of the signal---in a
sense, requiring small average coherence replaces the need for such an
assumption
Modulated Unit-Norm Tight Frames for Compressed Sensing
In this paper, we propose a compressed sensing (CS) framework that consists
of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a
column-wise orthonormal matrix. We prove that this structure satisfies the
restricted isometry property (RIP) with high probability if the number of
measurements for -sparse signals of length
and if the column-wise orthonormal matrix is bounded. Some existing structured
sensing models can be studied under this framework, which then gives tighter
bounds on the required number of measurements to satisfy the RIP. More
importantly, we propose several structured sensing models by appealing to this
unified framework, such as a general sensing model with arbitrary/determinisic
subsamplers, a fast and efficient block compressed sensing scheme, and
structured sensing matrices with deterministic phase modulations, all of which
can lead to improvements on practical applications. In particular, one of the
constructions is applied to simplify the transceiver design of CS-based channel
estimation for orthogonal frequency division multiplexing (OFDM) systems.Comment: submitted to IEEE Transactions on Signal Processin
Parametric dictionary design for sparse coding
Abstract—This paper introduces a new dictionary design method for sparse coding of a class of signals. It has been shown that one can sparsely approximate some natural signals using an overcomplete set of parametric functions, e.g. [1], [2]. A problem in using these parametric dictionaries is how to choose the parameters. In practice these parameters have been chosen by an expert or through a set of experiments. In the sparse approximation context, it has been shown that an incoherent dictionary is appropriate for the sparse approximation methods. In this paper we first characterize the dictionary design problem, subject to a constraint on the dictionary. Then we briefly explain that equiangular tight frames have minimum coherence. The complexity of the problem does not allow it to be solved exactly. We introduce a practical method to approximately solve it. Some experiments show the advantages one gets by using these dictionaries
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