38 research outputs found
Image Fusion via Sparse Regularization with Non-Convex Penalties
The L1 norm regularized least squares method is often used for finding sparse
approximate solutions and is widely used in 1-D signal restoration. Basis
pursuit denoising (BPD) performs noise reduction in this way. However, the
shortcoming of using L1 norm regularization is the underestimation of the true
solution. Recently, a class of non-convex penalties have been proposed to
improve this situation. This kind of penalty function is non-convex itself, but
preserves the convexity property of the whole cost function. This approach has
been confirmed to offer good performance in 1-D signal denoising. This paper
demonstrates the aforementioned method to 2-D signals (images) and applies it
to multisensor image fusion. The problem is posed as an inverse one and a
corresponding cost function is judiciously designed to include two data
attachment terms. The whole cost function is proved to be convex upon suitably
choosing the non-convex penalty, so that the cost function minimization can be
tackled by convex optimization approaches, which comprise simple computations.
The performance of the proposed method is benchmarked against a number of
state-of-the-art image fusion techniques and superior performance is
demonstrated both visually and in terms of various assessment measures
Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System
This paper studies the issue of sparsity adaptive channel reconstruction in time-varying cooperative
communication networks through the amplify-and-forward transmission scheme. A new sparsity adaptive system
identification method is proposed, namely reweighted norm ( < < ) penalized least mean square(LMS)algorithm.
The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing
so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification.
Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper
bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm.
With the upper bounds, we prove that the ( < < ) norm sparsity inducing cost function is superior to the
reweighted norm. An optimal selection of for the norm problem is studied to recover various sparse channel
vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus
demonstrate that the proposed algorithm has a better convergence speed and better steady state behavior than other LMS
algorithms
Ship Wake Detection in SAR Images via Sparse Regularization
In order to analyse synthetic aperture radar (SAR) images of the sea surface,
ship wake detection is essential for extracting information on the wake
generating vessels. One possibility is to assume a linear model for wakes, in
which case detection approaches are based on transforms such as Radon and
Hough. These express the bright (dark) lines as peak (trough) points in the
transform domain. In this paper, ship wake detection is posed as an inverse
problem, which the associated cost function including a sparsity enforcing
penalty, i.e. the generalized minimax concave (GMC) function. Despite being a
non-convex regularizer, the GMC penalty enforces the overall cost function to
be convex. The proposed solution is based on a Bayesian formulation, whereby
the point estimates are recovered using maximum a posteriori (MAP) estimation.
To quantify the performance of the proposed method, various types of SAR images
are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The
performance of various priors in solving the proposed inverse problem is first
studied by investigating the GMC along with the L1, Lp, nuclear and total
variation (TV) norms. We show that the GMC achieves the best results and we
subsequently study the merits of the corresponding method in comparison to two
state-of-the-art approaches for ship wake detection. The results show that our
proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
Ship wake detection is of great importance in the characterisation of
synthetic aperture radar (SAR) images of the ocean surface since wakes usually
carry essential information about vessels. Most detection methods exploit the
linear characteristics of the ship wakes and transform the lines in the spatial
domain into bright or dark points in a transform domain, such as the Radon or
Hough transforms. This paper proposes an innovative ship wake detection method
based on sparse regularisation to obtain the Radon transform of the SAR image,
in which the linear features are enhanced. The corresponding cost function
utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is
proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm
(MYULA), which is computationally efficient and robust is used to estimate the
image in the transform domain by minimizing the negative log-posterior
distribution. The detection accuracy of the Cauchy prior based approach is
86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.Comment: 9 pages, 2 Figures and 2 Table
Imposing early and asymptotic constraints on LiGME with application to nonconvex enhancement of fused lasso models
For the constrained LiGME model, a nonconvexly regularized least squares
estimation model, under its overall convexity condition, we newly present an
iterative algorithm of guaranteed convergence to its globally optimal solution.
The proposed algorithm can deal with two different types of constraints
simultaneously. The first type called the asymptotic constraint requires for
the limit point of the produced sequence by the proposed algorithm to achieve
asymptotically. The second type called the early constraint requires for the
whole sequence by the proposed algorithm to satisfy. We also propose a
nonconvex and constraint enhancement of fused lasso models for sparse piecewise
constant signal estimations, possibly under nonzero baseline assumptions, to
which the proposed enhancement with two types of constraints can achieve
robustness against possible model mismatch as well as higher estimation
accuracy compared with conventional fused lasso type models.Comment: 5 pages, 7 figure