The final presentation of gpu project.

1. 薛健鑫, 蔡昆修, 何星翰
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2.  Introduction
 Work progress
 SR Algorithm
 Evaluation
 SR video player demo
 Conclusion
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3.  Super-resolution are techniques that improve
image quality from low-resolution.
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4.  Multiple neighboring frames of video each provide a
rich amount of information about scene details.
 Super-resolution on video can analyze multiple
frames to reconstruct a enhance frame with detail
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5.  Retrieving image stream
 From OpenCV
 Super-resolution processing
 Algorithm : FGSR, FRSR
 GPU speedup investigation
 Video player
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6.  Fast General Super-Resolution (FGSR)
 For general video use
 Fast, cheap, bad result
 Fast & Robust Super-Resolution(FRSR)
 Only appropriate for translational motion
 Slow, better result
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7.  Predict HR = bicubic( Gi )
 Iterative fix Predict HR :
In’ = optcial_flow( bicubic(Gi+n ) )
Predict HR += α * ( Predict HR – In’ )
*
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8. Processing time
500
450
400
350
300
time (ms)
250
cpu
200 gpu
150
100
50
0
64 128 256 512 1024 2048
Image size
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9. Bilinear interpolation FGSR
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10.  Predicted HR= Median( shift(LR0~n) )
 Iterative improve Predicted HR :
Gback = FastGradientBackProject(HR);
Greg = GradientRegularization(HR);
HR = HR - β*(Gback + α* Greg);
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11. Bilinear interpolation FRSR
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12.  PSNR (Peak Signal to Noise Ratio):
Ground truth Bilinear interpolation Bicubic
interpolation
image
PSNR ∞ 14.450087 15.79615
(dB)
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13.  PSNR (Peak Signal to Noise Ratio):
Ground truth FGSR FRSR
algorithm algorithm
image
PSNR ∞ 14.642818 15.150817
(dB)
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14. 14

15.  Problem on super-resolution:
 Optical flow
 Occlusion/ disocclusion
 Insufficient information from neighboring frames
 Still have much space to improve
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16.  Sina Farsiu, Dirk Robinson, Michael Elad, Peyman Milanfar.
Fast and Robust Multi-Frame Super-Resolution. In IEEE
Transactions on Image Processing2003.
 Zhongding Jiang, Tien-tsin Wong, Hujun Bao. Practical
Super-Resolution from Dynamic Video Sequences. In Proc. of
IEEE CVPR2003.
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17. THE END
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