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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 40; H. 1; S. 192 - 207
Hauptverfasser: Ham, Bumsub, Minsu Cho, Ponce, Jean
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2017.2669034