Noisy Video Super-Resolution - PowerPoint PPT Presentation

feng liu jinjunwang shenghuozhu mm 08 university of wisconsin madison nec laboratories america inc n.
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Noisy Video Super-Resolution

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  1. Feng Liu, JinjunWang,ShenghuoZhu (MM’08) University of Wisconsin-Madison, NEC Laboratories America, Inc. Noisy Video Super-Resolution 第一組: 資訊四 B95902105 黃彥達 資訊碩一 R98922046 蔡旻光 網媒碩二 R97944012 鄒志鴻

  2. Outline • Introduction • Goal • File Format • Noise Reduced Image • Proposed Approach • Motion Estimation & Estimated Super-Resolution Result • Implementation • Result • Conclusion

  3. Introduction • Low-quality videos often not only have limited resolution but also suffer from noise • In fact, the requirements of de-noising & super-resolution is quite similar • This paper present a unified framework which achieves simultaneous video de-noising and super-resolution algorithm by some measurements of visual quality

  4. Goal • Refine low-quality videos from YouTube, and make the video better effects, which has better quality by human eyes. • Input is low-quality and noise-included (block effects or somewhat noise) videos

  5. File Format • .3gp file • Frame rate: 15(our video) or 25 • Frame size: 176(w) * 144(h) • MPEG-4 Part 12 • It is used on 3G mobile phones also can be played on 2G and 4G phones. • Our video: 867 KB/ 98 sec

  6. Noise-Reduced Image mv-SAD Gaussian-space Gaussian-time | p(I,j) – p(i’, j’) | > threshold

  7. Gaussian Space Set Mean = 0 Standard deviation Frame t Pixel(I,j)

  8. Motion Vector (mv_i, mv_j) Frame t+1 Pixel ( i , j , t) Frame t Pixel ( i + mv_i , j + mv_j , t+1)

  9. Gaussian Time Pixel(I,j) Shot Detection Frame t+2 Frame t - 2 Frame t - 1 Frame t Frame t+1 Frame t Time Gaussian Space Gaussian

  10. Noise-Reduced Image Before After

  11. Proposed Approach – 1 / 4 • Consider the visual quality with respect to the following 3 aspects: • Fidelity Preserving • To achieve similar high-resolution result • Detail Preserving • Enhanced details (edge) • Spatial-Temporal Smoothness • Remove undesirable high-frequency contents (e.g. jitter)

  12. Proposed Approach – 2 / 4 • Fidelity Preserving • Conventional metrics: • Measure fidelity by the difference between Ih & Il would be problematic & waste useful time-space information in video • Proposed metrics: • Estimate an approximation of super-resolution results from space-time neighboring pixels • The fidelity measurement: noised see next page for details

  13. Proposed Approach – 3 / 4 • Detail Preserving • Enhanced details (edge) • Contrast preserving • Human visual system is more sensitive to contrast than pixel values • Gradient fields of Ih & should be close ,where Wkis one or zero if the patchk with/o edges (canny detector)

  14. Proposed Approach – 4 / 4 • (Spatial-Temporal) Smoothness • Smooth results are often favored by the human system • Encourage to minimize: • A 2-D Laplace filter may be Spatial-temporal Laplacian OR

  15. An Optimization Problem • Proposed Measurements • A quadratic minimization problem to solve (AX = b): Similarity Contrast Detail Information(edge) Spatial-Temporal Smoothness

  16. Implementation – 1 / 3 Motion Estimation + Gaussian filter Inputlow Fidelity -1 0 1 … 1 -1 0 1 … 1 -1 0 1 … 1 Gradient = Minimize Edge 6 -1 … -1 -1 6 -1 … -1 -1 6 -1 … -1 Result (X) Laplacian

  17. Implementation – 2 / 3 Fidelity -1 0 1 … 1 -1 0 1 … 1 -1 0 1 … 1 Gradient = Minimize by sparse least square solver Edge 6 -1 … -1 -1 6 -1 … -1 -1 6 -1 … -1 Laplacian

  18. Implementation – 3 / 3 • Adjustments for the weight terms • The measurement term is more emphasized if the weight is larger • By iteratively experiments for our test data, we took • However, we found that the best weight set may be different for different videos

  19. Result • 352 x 288 Result

  20. Result • 352 x 288 Result

  21. Result • 352 x 288 Result

  22. Conclusion • The proposed framework formulates noisy video super-resolution as an optimization problem, aiming to maximize the visual quality • By exploiting the space-temporal information, we can estimate a better baseline than conventional fidelity measurement • The properties include fidelity-preserving, detail-preserving and smoothness are considered to achieve the best visual quality results

  23. Thank you!!