Robust Guided Image Filtering Using Nonconvex Potentials

Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transf...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 40; číslo 1; s. 192 - 207
Hlavní autoři:	Ham, Bumsub, Minsu Cho, Ponce, Jean
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	Algorithms Color Computer Science Computer vision Computer Vision and Pattern Recognition Guided image filtering Image color analysis Image edge detection Image filters Image restoration joint image filtering Linear programming majorize-minimization algorithm Noise reduction nonconvex optimization Optimization Outliers (statistics) Photography Robustness Signal processing Majorize-minimization algorithm Guided image filtering Joint image filtering Nonconvex optimization
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input images. Guided image filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
AbstractList	Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input images. Guided image filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising. Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input images. Guided image filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input images. Guided image filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
Author	Ponce, Jean Minsu Cho Ham, Bumsub
Author_xml	– sequence: 1 givenname: Bumsub surname: Ham fullname: Ham, Bumsub email: mimo@yonsei.ac.kr organization: Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea – sequence: 2 surname: Minsu Cho fullname: Minsu Cho email: mscho@postech.ac.kr organization: Dept. of Comput. Sci. & Eng., POSTECH, Pohang, South Korea – sequence: 3 givenname: Jean surname: Ponce fullname: Ponce, Jean email: jean.ponce@ens.fr organization: ENS, PSL Res. Univ. &, Paris, France
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Keywords	Majorize-minimization algorithm Guided image filtering Joint image filtering Nonconvex optimization
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Snippet	Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational...
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SubjectTerms	Algorithms Color Computer Science Computer vision Computer Vision and Pattern Recognition Guided image filtering Image color analysis Image edge detection Image filters Image restoration joint image filtering Linear programming majorize-minimization algorithm Noise reduction nonconvex optimization Optimization Outliers (statistics) Photography Robustness Signal processing
Title	Robust Guided Image Filtering Using Nonconvex Potentials
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