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Scale-Aware Guided and Structure-Preserved Texture Filter

  • Wei Cao
  • , Shiqian Wu
  • , Zhaoyi Liu
  • , Sos Agaian
  • Wuhan University of Science and Technology
  • The City University of New York

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this letter, a new texture filter with scale-aware gradients and structural preservation is proposed. The proposed filter uses a hybrid L_{0} - H{-1} variational model via measuring sparsity with scale-aware gradients by the L_{0} norm and structural fidelity by the H{-1} norm with the embedded Laplacian operator. Extensive qualitative and quantitative experimental results demonstrate that the proposed method 1) smooths small-scale low/high contrast textures and intensive noise while preserving sharp and prominent structures simultaneously; 2) significantly outperforms state-of-the-art texture filtering methods; and 3) has fast convergence.

Original languageEnglish
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
StatePublished - 2022
Externally publishedYes

Keywords

  • H⁻¹ norm
  • Lnorm
  • indirect image smoothing
  • scale-aware sparse term
  • structure-preserving fidelity term

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