Convex denoising using non-convex tight frame regularization
A Parekh, IW Selesnick - IEEE Signal Processing Letters, 2015 - ieeexplore.ieee.org
IEEE Signal Processing Letters, 2015•ieeexplore.ieee.org
This letter considers the problem of signal denoising using a sparse tight-frame analysis
prior. The l1 norm has been extensively used as a regularizer to promote sparsity; however,
it tends to under-estimate non-zero values of the underlying signal. To more accurately
estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to
ensure convexity of the objective function. The convexity of the objective function is ensured
by constraining the parameter of the non-convex penalty. We use ADMM to obtain a solution …
prior. The l1 norm has been extensively used as a regularizer to promote sparsity; however,
it tends to under-estimate non-zero values of the underlying signal. To more accurately
estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to
ensure convexity of the objective function. The convexity of the objective function is ensured
by constraining the parameter of the non-convex penalty. We use ADMM to obtain a solution …
This letter considers the problem of signal denoising using a sparse tight-frame analysis prior. The l1 norm has been extensively used as a regularizer to promote sparsity; however, it tends to under-estimate non-zero values of the underlying signal. To more accurately estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to ensure convexity of the objective function. The convexity of the objective function is ensured by constraining the parameter of the non-convex penalty. We use ADMM to obtain a solution and show how to guarantee that ADMM converges to the global optimum of the objective function. We illustrate the proposed method for 1D and 2D signal denoising.
ieeexplore.ieee.org
Showing the best result for this search. See all results