Defocus and Motion Blur Detection with Deep Contextual Features
Supplementary Material
Beomseok Kim
1, Hyeongseok Son
1, Seong‐Jin Park
1, Sunghyun Cho
2, and Seungyong Lee
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POSTECH
2DGIST
Contents
• More comparisons with a state‐of‐the‐art method in blur detection on CUHK test set
• Huang et al., “Multiscale blur detection by learning discriminative deep features”, Neurocomputing, 2018.
• Challenging examples with mixed blur
• Objects outside the depth‐of‐field are motion‐blurred
• Examples of moving object segmentation
• More results with real blurred photographs
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More comparisons of blur detection results on CUHK test set
input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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input
Huang et el. ours
ground-truth
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Challenging examples with mixed blur
(Objects outside the depth‐of‐field are motion‐blurred) (blue: motion blur, red: defocus blur, black: no‐blur)
Our method returns the dominant blur type at each pixel in this case.
input output
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input output
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input output
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input output
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input output
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Examples of moving object segmentation with real photographs
(blue: motion blur, red: defocus blur, black: no‐blur)
input output
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input output
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input output
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input output
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input output
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input output
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input output
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input output 39
More examples with real blurred photographs
(blue: motion blur, red: defocus blur, black: no‐blur)
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