Video Enhancement 視訊優化

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數位典藏國家型科技計畫 / 中央研究院 版權所有 Copyright © National Digital Archives Program / Academia Sinica Video Enhancement 視訊優化 Video Enhancement Video Enhancement 視訊優化 視訊優化 Su, Su, Chih Chih - - Wen Wen ( ( 蘇志文 蘇志文 ) ) [email protected] [email protected]

Transcript of Video Enhancement 視訊優化

Video Enhancement Video Enhancement
• (video enhancement) • (digital archives)


(video) (image) (frame)


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Image and video coloring
Fast Image and Video Colorization Using Chrominance Blending
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Fast Image and Video Colorization Using Chrominance Blending
/  Copyright © National Digital Archives Program / Academia Sinica
Fast Image and Video Colorization Using Chrominance Blending
/  Copyright © National Digital Archives Program / Academia Sinica
A Texture Recognition Coloring Technique for Natural Gray Images
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A Texture Recognition Coloring Technique for Natural Gray Images
/  Copyright © National Digital Archives Program / Academia Sinica
Content-Aware Image Resizing

• ””
( > > ) ( > > )
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing •

(Image inpainting) •
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/ •

Chan and Shen, 00’ • Partial differential equation

Oliveira et al., 01’ • Ω:a small area to be inpainted.
∂Ω: The boundary of Ω.
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Yamauchi et al., 03’ •
- PDE
(Discrete Cosine Transform)
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Flowchart • The input image I is decomposed into a
high-frequency part H and a low- frequency part L using DCT.
• The fast image inpainting algorithm is applied to the interior of the masked areas of the low-frequency image L to obtain the inpainted image L*.
• The high-frequency image H is decomposed into a Gaussian pyramid with n+1 levels Hi (i = 0, . . . , n).
• Starting from the highest level Hn, we apply multiresolution texture synthesis inside the masked areas in Hi.
• The synthesized high-frequency image H0* and the inpainted low- frequency image L* are summed up to yield I*.
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• 1.7 GHz Pentium4 PC, image size of 600×450, masks covered 4–6% of the input image. For these masks, the algorithm took about 5–10 min to complete.
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Exemplar-based inpainting (Criminisi et al., 03’)
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Region Filling Algorithm


Confidence and color of coarse level
Confidence and color of fine level
Fragment-Based Image Completion (Drori et al. 03’)
• Completing the image from coarse to fine.
From left to right: inverse matte, confidence map (G=1, B=0.01~1, P<0.01), level set of candidate positions.
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• Iteratively approximates the unknown region and fills in the image by adaptive fragments. The visible parts of the image serve as a training set to infer the unknown parts.
Fragment-Based Image Completion (Drori et al. 03’)
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• 192x 128 : 120 -419 (2.4 GHz CPU) • 384 x 256 : 83 -158 (2.4 GHz CPU) • 90%
• (ambiguous):
Video inpainting under constrained camera motion
(IP 2007)

Space-time completion of video (PAMI 2007)
• 3D

http://member.mine.tku.edu.tw/www/T_CSVT/web/



A Texture Recognition Coloring Technique for Natural Gray Images
A Texture Recognition Coloring Technique for Natural Gray Images
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing
Content-Aware Image Resizing
Region Filling Algorithm