Structure Adaptive Total Variation Minimization-Based Image Decomposition

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

doi:10.1109/TCSVT.2017.2717542

Structure Adaptive Total Variation Minimization-Based Image Decomposition

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

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

28 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 language	English
Article number	7953643
Pages (from-to)	2164-2176
Number of pages	13
Journal	IEEE Transactions on Circuits and Systems for Video Technology
Volume	28
Issue number	9
DOIs	https://doi.org/10.1109/TCSVT.2017.2717542
State	Published - Sep 2018

Bibliographical note

Funding Information:
Manuscript received December 1, 2016; revised May 11, 2017; accepted June 14, 2017. Date of publication June 20, 2017; date of current version September 13, 2018. This work was supported in part by the Basic Science Research Program (3D reconstruction of high-resolution optical microscopy software development) through the National Research Foundation of Korea, Korean Ministry of Education under Grant NRF-2016R1D1A1B04932889, in part by the Institute for Information and Communications Technology Promotion through the Korean government under Grant R0115-16-1009, and in part by the Basic Science Research Program through the National Research Foundation of Korea, Korean government (MSIP) under Grant NRF-2017R1A2B4011015. The work of J. Yoon was supported by the National Research Foundation of Korea under Grant NRF-2015-R1A5A1009350 and Grant NRF-2015-R1D1A1A09057553. This paper was recommended by Associate Editor L. Shao. (Corresponding author: Sang Min Yoon.) J. Song, H. Cho, and S. M. Yoon are with the HCI Lab, College of Computer Science, Kookmin University, Seoul 02707, South Korea (e-mail: [email protected]).

Publisher Copyright:
© 1991-2012 IEEE.

Keywords

Image decomposition
image enhancement
total variation minimization

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10.1109/TCSVT.2017.2717542

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title = "Structure Adaptive Total Variation Minimization-Based Image Decomposition",

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.",

keywords = "Image decomposition, image enhancement, total variation minimization",

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note = "Funding Information: Manuscript received December 1, 2016; revised May 11, 2017; accepted June 14, 2017. Date of publication June 20, 2017; date of current version September 13, 2018. This work was supported in part by the Basic Science Research Program (3D reconstruction of high-resolution optical microscopy software development) through the National Research Foundation of Korea, Korean Ministry of Education under Grant NRF-2016R1D1A1B04932889, in part by the Institute for Information and Communications Technology Promotion through the Korean government under Grant R0115-16-1009, and in part by the Basic Science Research Program through the National Research Foundation of Korea, Korean government (MSIP) under Grant NRF-2017R1A2B4011015. The work of J. Yoon was supported by the National Research Foundation of Korea under Grant NRF-2015-R1A5A1009350 and Grant NRF-2015-R1D1A1A09057553. This paper was recommended by Associate Editor L. Shao. (Corresponding author: Sang Min Yoon.) J. Song, H. Cho, and S. M. Yoon are with the HCI Lab, College of Computer Science, Kookmin University, Seoul 02707, South Korea (e-mail: [email protected]). Publisher Copyright: {\textcopyright} 1991-2012 IEEE.",

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Structure Adaptive Total Variation Minimization-Based Image Decomposition

Abstract

Bibliographical note

Keywords

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