Structure Adaptive Total Variation Minimization-Based Image Decomposition

Jinjoo Song, Heeryon Cho, Jungho Yoon, Sang Min Yoon

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Structure-preserving image decomposition separates a given image into structure and texture by smoothing the image, simultaneously preserving or enhancing image edges. The well-studied problem of image decomposition is applied to various areas, such as image smoothing, detail enhancement, non-photorealistic rendering, image artistic rendering, and high-dynamic-range compression. In this paper, we propose a fast algorithm for structure-preserving image decomposition that adopts total variation (TV) minimization to the moving least squares (MLS) method with non-local weights, called structure adaptive TV (SATV) minimization. MLS with non-local weights provides high accuracy approximation that is robust to noise, and allows a fast convergence with TV regularization term. As a result, our proposed SATV preserves the dominant structure while flattening fine-scale details. The experimental results show that the SATV minimization algorithm provides faster and more robust image decomposition than the well-known previous approaches. We demonstrate the usefulness of our algorithm by presenting successful applications in image smoothing and detail enhancement.

Original languageEnglish
Article number7953643
Pages (from-to)2164-2176
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume28
Issue number9
DOIs
StatePublished - Sep 2018

Keywords

  • Image decomposition
  • image enhancement
  • total variation minimization

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