Nonconvex Haar-TV denoising

Research output: Contribution to journalArticlepeer-review

Abstract

The anisotropic total variation (TV) denoising model suppresses noise for two-dimensional signals that are vertically and horizontally piecewise constant. However, two-dimensional signals may have sparse derivatives in other directions. We propose a modification of the classical anisotropic two-dimensional TV regularizer from a spectral point of view. In the frequency domain, the TV regularizer can be considered as penalizing the high-frequency component of original signals and promoting only low-frequency components. The classical anisotropic TV, which applies l1-norm on vertical and horizontal differences, suppresses high-frequency components of the signals. The proposed operator, named Haar total variation (Haar-TV), penalizes two-dimensional signals that have more varied high-frequency regions. Furthermore, we propose non-convex penalties based on the Haar-TV operator since non-convex penalties can preserve edges and thus enhance the quality of the estimation. We derive a condition that preserves the strong convexity of the total cost function so the global minimizer can be reached.

Original languageEnglish (US)
Article number102855
JournalDigital Signal Processing: A Review Journal
Volume107
DOIs
StatePublished - Dec 2020

Keywords

  • Denoising
  • Haar total variation (Haar-TV)
  • Image denoising
  • Nonconvex penalties
  • Piecewise constant (PWC) signals

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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