5 research outputs found
Recovery analysis for weighted mixed minimization with
We study the recovery conditions of weighted mixed minimization for block sparse signal reconstruction from compressed
measurements when partial block support information is available. We show that
the block -restricted isometry property (RIP) can ensure the robust
recovery. Moreover, we present the sufficient and necessary condition for the
recovery by using weighted block -null space property. The relationship
between the block -RIP and the weighted block -null space property has
been established. Finally, we illustrate our results with a series of numerical
experiments
Support Driven Wavelet Frame-based Image Deblurring
The wavelet frame systems have been playing an active role in image
restoration and many other image processing fields over the past decades, owing
to the good capability of sparsely approximating piece-wise smooth functions
such as images. In this paper, we propose a novel wavelet frame based sparse
recovery model called \textit{Support Driven Sparse Regularization} (SDSR) for
image deblurring, where the partial support information of frame coefficients
is attained via a self-learning strategy and exploited via the proposed
truncated regularization. Moreover, the state-of-the-art image
restoration methods can be naturally incorporated into our proposed wavelet
frame based sparse recovery framework. In particular, in order to achieve
reliable support estimation of the frame coefficients, we make use of the
state-of-the-art image restoration result such as that from the IDD-BM3D method
as the initial reference image for support estimation. Our extensive
experimental results have shown convincing improvements over existing
state-of-the-art deblurring methods
Robust width: A characterization of uniformly stable and robust compressed sensing
Compressed sensing seeks to invert an underdetermined linear system by
exploiting additional knowledge of the true solution. Over the last decade,
several instances of compressed sensing have been studied for various
applications, and for each instance, reconstruction guarantees are available
provided the sensing operator satisfies certain sufficient conditions. In this
paper, we completely characterize the sensing operators which allow uniformly
stable and robust reconstruction by convex optimization for many of these
instances. The characterized sensing operators satisfy a new property we call
the robust width property, which simultaneously captures notions of widths from
approximation theory and of restricted eigenvalues from statistical regression.
We provide a geometric interpretation of this property, we discuss its
relationship with the restricted isometry property, and we apply techniques
from geometric functional analysis to find random matrices which satisfy the
property with high probability.Comment: 24 page
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
This work proposes and analyzes a compressed sensing approach to polynomial
approximation of complex-valued functions in high dimensions. Of particular
interest is the setting where the target function is smooth, characterized by a
rapidly decaying orthonormal expansion, whose most important terms are captured
by a lower (or downward closed) set. By exploiting this fact, we present an
innovative weighted minimization procedure with a precise choice of
weights, and a new iterative hard thresholding method, for imposing the
downward closed preference. Theoretical results reveal that our computational
approaches possess a provably reduced sample complexity compared to existing
compressed sensing techniques presented in the literature. In addition, the
recovery of the corresponding best approximation using these methods is
established through an improved bound for the restricted isometry property. Our
analysis represents an extension of the approach for Hadamard matrices in [5]
to the general case of continuous bounded orthonormal systems, quantifies the
dependence of sample complexity on the successful recovery probability, and
provides an estimate on the number of measurements with explicit constants.
Numerical examples are provided to support the theoretical results and
demonstrate the computational efficiency of the novel weighted
minimization strategy.Comment: 33 pages, 3 figure
Correcting for unknown errors in sparse high-dimensional function approximation
We consider sparsity-based techniques for the approximation of
high-dimensional functions from random pointwise evaluations. To date, almost
all the works published in this field contain some a priori assumptions about
the error corrupting the samples that are hard to verify in practice. In this
paper, we instead focus on the scenario where the error is unknown. We study
the performance of four sparsity-promoting optimization problems: weighted
quadratically-constrained basis pursuit, weighted LASSO, weighted square-root
LASSO, and weighted LAD-LASSO. From the theoretical perspective, we prove
uniform recovery guarantees for these decoders, deriving recipes for the
optimal choice of the respective tuning parameters. On the numerical side, we
compare them in the pure function approximation case and in applications to
uncertainty quantification of ODEs and PDEs with random inputs. Our main
conclusion is that the lesser-known square-root LASSO is better suited for
high-dimensional approximation than the other procedures in the case of bounded
noise, since it avoids (both theoretically and numerically) the need for
parameter tuning