research-article

Edge-preserving image decomposition using L1 fidelity with L0 gradient

Authors:

Chih-Tsung Shen,

Soo-Chang PeiAuthors Info & Claims

SA '12: SIGGRAPH Asia 2012 Technical Briefs

Article No.: 6, Pages 1 - 4

https://doi.org/10.1145/2407746.2407752

Published: 28 November 2012 Publication History

Abstract

We present an image decomposition method using L1 fidelity term with L0 norm of gradient to decompose an image into base layer and detail layer. Generally, the L1 fidelity should be preferable to the L2 norm when the erroneous measurements exist. It is also reported that the L0 norm of gradient is a better prior term than total variation and the L2 norm of gradient. Therefore, we combine these two benefits to obtain our base layer by adopting our method using L1 fidelity and L0 gradient. Our image decomposition method can be regarded as the fundamental tool to generate multiple image editing applications, such as image denoising, edge detection, detail enhancement, cartoon JPEG artifact removal, local tone mapping, and contrast enhancement under low backlight condition. Experimental results show that our proposed method is promising as compared to the existing methods.

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Cited By

Sun XLv XZhu GFang BJiang L(2023) Fast additive half‐quadratic iterative minimization for l p − l q image smoothing IET Image Processing10.1049/ipr2.1275117:6(1739-1751)Online publication date: 8-Feb-2023
https://doi.org/10.1049/ipr2.12751
Bhutto JTian LDu QSun ZYu LSoomro T(2022)An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer LearningRemote Sensing10.3390/rs1404093914:4(939)Online publication date: 15-Feb-2022
https://doi.org/10.3390/rs14040939
Yan HZhang JZhang X(2022)Injected Infrared and Visible Image Fusion via $L_{1}$ Decomposition Model and Guided FilteringIEEE Transactions on Computational Imaging10.1109/TCI.2022.31514728(162-173)Online publication date: 2022
https://doi.org/10.1109/TCI.2022.3151472
Show More Cited By

Index Terms

Edge-preserving image decomposition using L1 fidelity with L0 gradient
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
    2. Rendering

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Published In

cover image ACM Conferences

SA '12: SIGGRAPH Asia 2012 Technical Briefs

November 2012

144 pages

ISBN:9781450319157

DOI:10.1145/2407746

Conference Chairs:
Zhengyou Zhang
Microsoft Research
,
Zhengguo Li
Institute for Infocomm Research, A*STAR

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2012

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SA '12

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SA '12: SIGGRAPH Asia 2012

November 28 - December 1, 2012

Singapore, Singapore

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Citations

Cited By

Sun XLv XZhu GFang BJiang L(2023) Fast additive half‐quadratic iterative minimization for l p − l q image smoothing IET Image Processing10.1049/ipr2.1275117:6(1739-1751)Online publication date: 8-Feb-2023
https://doi.org/10.1049/ipr2.12751
Bhutto JTian LDu QSun ZYu LSoomro T(2022)An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer LearningRemote Sensing10.3390/rs1404093914:4(939)Online publication date: 15-Feb-2022
https://doi.org/10.3390/rs14040939
Yan HZhang JZhang X(2022)Injected Infrared and Visible Image Fusion via $L_{1}$ Decomposition Model and Guided FilteringIEEE Transactions on Computational Imaging10.1109/TCI.2022.31514728(162-173)Online publication date: 2022
https://doi.org/10.1109/TCI.2022.3151472
Li JFeng L(2020)Rolling Guidance Filtering-Orientated Saliency Region Extraction Method for Visible and Infrared Images FusionSensing and Imaging10.1007/s11220-020-00282-721:1Online publication date: 10-Mar-2020
https://doi.org/10.1007/s11220-020-00282-7
Wu CNeumann U(2019)Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing2019 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2019.8803054(944-948)Online publication date: Sep-2019
https://doi.org/10.1109/ICIP.2019.8803054
Yang CFeng HXu ZLi QChen Y(2019)Correction of overexposure utilizing haze removal model and image fusion techniqueThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-018-1504-z35:5(695-705)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s00371-018-1504-z
Wang DZou CLi GGao CSu ZTan P(2017)ℒ0 Gradient‐Preserving Color TransferComputer Graphics Forum10.1111/cgf.1327536:7(93-103)Online publication date: 13-Oct-2017
https://doi.org/10.1111/cgf.13275
Ono S(2017)$L_{0}$ Gradient ProjectionIEEE Transactions on Image Processing10.1109/TIP.2017.265139226:4(1554-1564)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2651392
Lu KYou SBarnes N(2017)Double-Guided Filtering: Image Smoothing with Structure and Texture Guidance2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA.2017.8227425(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/DICTA.2017.8227425
Kochher RGoyal S(2016)Suppressing wrinkles and enhancing human face using L0 norm and ACO algorithm2016 10th International Conference on Intelligent Systems and Control (ISCO)10.1109/ISCO.2016.7727124(1-6)Online publication date: Jan-2016
https://doi.org/10.1109/ISCO.2016.7727124
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