Convex 1-D Total Variation Denoising with Non-convex Regularization


Selesnick I. W. , Parekh A., Bayram I.

IEEE SIGNAL PROCESSING LETTERS, cilt.22, sa.2, ss.141-144, 2015 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Konu: 2
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/lsp.2014.2349356
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Sayfa Sayıları: ss.141-144

Özet

Total variation (TV) denoising is an effective noise suppression method when the derivative of the underlying signal is known to be sparse. TV denoising is defined in terms of a convex optimization problem involving a quadratic data fidelity term and a convex regularization term. A non-convex regularizer can promote sparsity more strongly, but generally leads to a non-convex optimization problem with non-optimal local minima. This letter proposes the use of a non-convex regularizer constrained so that the total objective function to be minimized maintains its convexity. Conditions for a non-convex regularizer are given that ensure the total TV denoising objective function is convex. An efficient algorithm is given for the resulting problem.