薛健鑫, 蔡昆修, 何星翰




                1
   Introduction
   Work progress
   SR Algorithm
   Evaluation
   SR video player demo
   Conclusion



            ...
   Super-resolution are techniques that improve
    image quality from low-resolution.




                              ...
   Multiple neighboring frames of video each provide a
    rich amount of information about scene details.
   Super-reso...
   Retrieving image stream
     From OpenCV
   Super-resolution processing
     Algorithm : FGSR, FRSR
     GPU speed...
   Fast General Super-Resolution (FGSR)
     For general video use
     Fast, cheap, bad result
   Fast & Robust Super...
   Predict HR = bicubic( Gi )
   Iterative fix Predict HR :
    In’ = optcial_flow( bicubic(Gi+n ) )
    Predict HR += α...
Processing time
            500

            450

            400

            350

            300
time (ms)




        ...
Bilinear interpolation   FGSR




                                9
   Predicted HR= Median( shift(LR0~n) )
   Iterative improve Predicted HR :
    Gback = FastGradientBackProject(HR);
   ...
Bilinear interpolation   FRSR




                                11
    PSNR (Peak Signal to Noise Ratio):
         Ground truth   Bilinear interpolation   Bicubic
                         ...
    PSNR (Peak Signal to Noise Ratio):
         Ground truth   FGSR              FRSR
                        algorithm  ...
14
   Problem on super-resolution:
     Optical flow
     Occlusion/ disocclusion
     Insufficient information from neig...
 Sina Farsiu, Dirk Robinson, Michael Elad, Peyman Milanfar.
  Fast and Robust Multi-Frame Super-Resolution. In IEEE
  Tra...
THE END

          17
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Gpgpu presentation final

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Gpgpu presentation final

  1. 1. 薛健鑫, 蔡昆修, 何星翰 1
  2. 2.  Introduction  Work progress  SR Algorithm  Evaluation  SR video player demo  Conclusion 2
  3. 3.  Super-resolution are techniques that improve image quality from low-resolution. 3
  4. 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 4
  5. 5.  Retrieving image stream  From OpenCV  Super-resolution processing  Algorithm : FGSR, FRSR  GPU speedup investigation  Video player 5
  6. 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 6
  7. 7.  Predict HR = bicubic( Gi )  Iterative fix Predict HR : In’ = optcial_flow( bicubic(Gi+n ) ) Predict HR += α * ( Predict HR – In’ ) * 7
  8. 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 8
  9. 9. Bilinear interpolation FGSR 9
  10. 10.  Predicted HR= Median( shift(LR0~n) )  Iterative improve Predicted HR : Gback = FastGradientBackProject(HR); Greg = GradientRegularization(HR); HR = HR - β*(Gback + α* Greg); 10
  11. 11. Bilinear interpolation FRSR 11
  12. 12.  PSNR (Peak Signal to Noise Ratio): Ground truth Bilinear interpolation Bicubic interpolation image PSNR ∞ 14.450087 15.79615 (dB) 12
  13. 13.  PSNR (Peak Signal to Noise Ratio): Ground truth FGSR FRSR algorithm algorithm image PSNR ∞ 14.642818 15.150817 (dB) 13
  14. 14. 14
  15. 15.  Problem on super-resolution:  Optical flow  Occlusion/ disocclusion  Insufficient information from neighboring frames  Still have much space to improve 15
  16. 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. 16
  17. 17. THE END 17
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