Efficient Color Image Segmentation via Quaternion-based Regularization
Color image segmentation is a key technology in image processing. In this paper, a two-
stage image segmentation method is proposed that is based on the nonconvex L 1/L 2 …
stage image segmentation method is proposed that is based on the nonconvex L 1/L 2 …
ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed?
This paper investigates the problem of stable signal estimation from undersampled, noisy
sub-Gaussian measurements under the assumption of a cosparse model. Based on …
sub-Gaussian measurements under the assumption of a cosparse model. Based on …
Living near the edge: A lower-bound on the phase transition of total variation minimization
This work is about the total variation (TV) minimization which is used for recovering gradient-
sparse signals from compressed measurements. Recent studies indicate that TV …
sparse signals from compressed measurements. Recent studies indicate that TV …
On the error in phase transition computations for compressed sensing
Evaluating the statistical dimension is a common tool to determine the asymptotic phase
transition in compressed sensing problems with Gaussian ensemble. Unfortunately, the …
transition in compressed sensing problems with Gaussian ensemble. Unfortunately, the …
Sample complexity of total variation minimization
This letter considers the use of total variation (TV) minimization in the recovery of a given
gradient sparse vector from Gaussian linear measurements. It has been shown in recent …
gradient sparse vector from Gaussian linear measurements. It has been shown in recent …
Effective condition number bounds for convex regularization
We derive bounds relating Renegar's condition number to quantities that govern the
statistical performance of convex regularization in settings that include the ℓ 1-analysis …
statistical performance of convex regularization in settings that include the ℓ 1-analysis …
Compressed sensing with 1D total variation: Breaking sample complexity barriers via non-uniform recovery
This paper investigates total variation minimization in one spatial dimension for the recovery
of gradient-sparse signals from undersampled Gaussian measurements. Recently …
of gradient-sparse signals from undersampled Gaussian measurements. Recently …
[BOOK][B] The Mismatch Principle and ℓ1-Analysis Compressed Sensing: A Unified Approach to Estimation Under Large Model Uncertainties and Structural Constraints
M Genzel - 2019 - search.proquest.com
This thesis contributes to several mathematical aspects and problems at the interface of
statistical learning theory and signal processing. Although based on a common theoretical …
statistical learning theory and signal processing. Although based on a common theoretical …
Theoretical and numerical approaches to co-/sparse recovery in discrete tomography
J Plier - 2020 - archiv.ub.uni-heidelberg.de
We investigate theoretical and numerical results that guarantee the exact reconstruction of
piecewise constant images from insufficient projections in Discrete Tomography. This is …
piecewise constant images from insufficient projections in Discrete Tomography. This is …
[BOOK][B] Phase Transitions in Generalized Compressed Sensing
J Walvin - 2019 - search.proquest.com
Applications in science and technology depend increasingly on vast amounts of data. In
many cases, the data we are considering can be modelled as a superposition, or sum, of …
many cases, the data we are considering can be modelled as a superposition, or sum, of …