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Qiaochu Li, Qikun Guo, Saboya Yang and Jiaying Liu* Institute of Computer Science and Technology Peking University Scale-Compensated Nonlocal Mean Super.

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Presentation on theme: "Qiaochu Li, Qikun Guo, Saboya Yang and Jiaying Liu* Institute of Computer Science and Technology Peking University Scale-Compensated Nonlocal Mean Super."— Presentation transcript:

1 Qiaochu Li, Qikun Guo, Saboya Yang and Jiaying Liu* Institute of Computer Science and Technology Peking University Scale-Compensated Nonlocal Mean Super Resolution 2013

2 2 Outline Introduction  Multi-frame SR  Nonlocal means SR (NLM SR) Our Algorithm  Scale-detector  Scale-Compensated NLM  Experimental results Conclusion & Future work

3 3 Outline Introduction  Multi-frame SR  Nonlocal means SR (NLM SR) Our Algorithm  Scale-detector  Scale-Compensated NLM  Experimental results Conclusion & Future work

4 4 Multi-Frame SR Converge low resolution images into a high resolution image  Direct motion estimation INVALID in complex situation

5 5 Nonlocal Means SR Image content repeats in neighborhoods  In temporal and spatial domains  Probabilistic motion estimation  Weighted average NLM weight distribution. The weights go from 1 (white) to 0 (black).

6 6 Problem Scale may be varied in frames by zooming.  Camera motion  Object motion Scale changing effects in adjacent frames. (a) Two adjacent frames, (b) some critical areas of the frames.

7 7 Outline Introduction  Multi-frame SR  Nonlocal means SR (NLM SR) Our Algorithm  Scale-detector  Scale-Compensated NLM  Experimental results Conclusion & Future work

8 8 Scale-Detector Using SIFT descriptor to compute scales Partial matched keypoints and the corresponding scale values.

9 9 Verification Verification of scale-detector Always appears region The performances of scale-detector in different standard scales and different resolutions, (a) average error by frame scale, (b) average error by frame resolution. (a) (b)

10 10 Scale-Compensated NLM SC NLM finds more similar patches Comparison of unmodified and modified patch-extractor in patch matching.

11 11 Procedures Overview of SC NLM Scale- detector Patch extraction & modification Patch extraction & modification NLM SR

12 12 Experimental Results Downsample  Blurred using 3×3 uniform mask  Decimated by 3× factor  Additive noise with standard deviation 2 Objective measurement Subjective measurement

13 13 Experimental Results 3×, Objective measurement (PSNR) SequenceNLMARI-SWRSC-NLM Foreman31.1530.9631.27 Tempete22.8522.7423.00 Text29.2330.0630.11 Man27.1427.0227.29

14 14 Experimental Results 3×, Subjective measurement (SSIM) SequenceNLMARI-SWRSC-NLM Foreman0.81090.80010.8151 Tempete0.69270.67370.7013 Text0.85920.85120.8633 Man0.77800.76170.7831

15 15 Experimental Results a) Result of whole frame. b) High resolution frame. c) NLM SR. d) SC NLM.

16 16 Experimental Results a) Result of whole frame. b) High resolution frame. c) NLM SR. d) SC NLM

17 17 Outline Introduction  Multi-frame SR  Nonlocal means SR (NLM SR) Our Algorithm  Scale-detector  Scale-Compensated NLM  Experimental results Conclusion & Future work

18 18 Conclusion When patches are convert into SAME SCALE, we can find more SIMILAR PATCHES, we can use more COMPLEMENTARY INFORMATION to reconstruct a HIGH RESOLUTION & QUANLITY IMAGE.

19 19 Future Work More accurate scale-detector  Segmentation based scale-detector Combination of rotation and translation- invariant algorithm  Rotation-invariant measurement  Translation-invariant measurement

20 20

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