62 research outputs found
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing.
Tensor compressive sensing (TCS) is a multidimensional framework of compressive sensing (CS), and it is
advantageous in terms of reducing the amount of storage, easing
hardware implementations, and preserving multidimensional
structures of signals in comparison to a conventional CS system.
In a TCS system, instead of using a random sensing matrix and
a predefined dictionary, the average-case performance can be
further improved by employing an optimized multidimensional
sensing matrix and a learned multilinear sparsifying dictionary.
In this paper, we propose an approach that jointly optimizes
the sensing matrix and dictionary for a TCS system. For the
sensing matrix design in TCS, an extended separable approach
with a closed form solution and a novel iterative nonseparable
method are proposed when the multilinear dictionary is fixed.
In addition, a multidimensional dictionary learning method that
takes advantages of the multidimensional structure is derived,
and the influence of sensing matrices is taken into account in the
learning process. A joint optimization is achieved via alternately
iterating the optimization of the sensing matrix and dictionary.
Numerical experiments using both synthetic data and real images
demonstrate the superiority of the proposed approache
Tight-frame-like Sparse Recovery Using Non-tight Sensing Matrices
The choice of the sensing matrix is crucial in compressed sensing (CS).
Gaussian sensing matrices possess the desirable restricted isometry property
(RIP), which is crucial for providing performance guarantees on sparse
recovery. Further, sensing matrices that constitute a Parseval tight frame
result in minimum mean-squared-error (MSE) reconstruction given oracle
knowledge of the support of the sparse vector. However, if the sensing matrix
is not tight, could one achieve the reconstruction performance assured by a
tight frame by suitably designing the reconstruction strategy? This is the key
question that we address in this paper. We develop a novel formulation that
relies on a generalized l2-norm-based data-fidelity loss that tightens the
sensing matrix, along with the standard l1 penalty for enforcing sparsity. The
optimization is performed using proximal gradient method, resulting in the
tight-frame iterative shrinkage thresholding algorithm (TF-ISTA). We show that
the objective convergence of TF-ISTA is linear akin to that of ISTA.
Incorporating Nesterovs momentum into TF-ISTA results in a faster variant,
namely, TF-FISTA, whose objective convergence is quadratic, akin to that of
FISTA. We provide performance guarantees on the l2-error for the proposed
formulation. Experimental results show that the proposed algorithms offer
superior sparse recovery performance and faster convergence. Proceeding
further, we develop the network variants of TF-ISTA and TF-FISTA, wherein a
convolutional neural network is used as the sparsifying operator. On the
application front, we consider compressed sensing image recovery (CSIR).
Experimental results on Set11, BSD68, Urban100, and DIV2K datasets show that
the proposed models outperform state-of-the-art sparse recovery methods, with
performance measured in terms of peak signal-to-noise ratio (PSNR) and
structural similarity index metric (SSIM).Comment: 33 pages, 12 figure
Sparse Recovery Analysis of Preconditioned Frames via Convex Optimization
Orthogonal Matching Pursuit and Basis Pursuit are popular reconstruction
algorithms for recovery of sparse signals. The exact recovery property of both
the methods has a relation with the coherence of the underlying redundant
dictionary, i.e. a frame. A frame with low coherence provides better guarantees
for exact recovery. An equivalent formulation of the associated linear system
is obtained via premultiplication by a non-singular matrix. In view of bounds
that guarantee sparse recovery, it is very useful to generate the
preconditioner in such way that the preconditioned frame has low coherence as
compared to the original. In this paper, we discuss the impact of
preconditioning on sparse recovery. Further, we formulate a convex optimization
problem for designing the preconditioner that yields a frame with improved
coherence. In addition to reducing coherence, we focus on designing well
conditioned frames and numerically study the relationship between the condition
number of the preconditioner and the coherence of the new frame. Alongside
theoretical justifications, we demonstrate through simulations the efficacy of
the preconditioner in reducing coherence as well as recovering sparse signals.Comment: 9 pages, 5 Figure
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