Feng Liu, JinjunWang,ShenghuoZhu (MM’08) University of Wisconsin-Madison, NEC Laboratories America, Inc. 第一組： 資訊四 B 黃彥達 資訊碩一 R 蔡旻光 網媒碩二 R 鄒志鴻

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

 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 3

 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

mv-SADGaussian-spaceGaussian-time | p(I,j) – p(i’, j’) | > threshold

Frame t Pixel(I,j) Standard deviation Set Mean = 0

Frame t Pixel ( i, j, t) Frame t+1 Pixel ( i + mv_i, j + mv_j, t+1) (mv_i, mv_j)

Frame t - 2Frame t - 1Frame t Space Gaussian Time Gaussian Pixel(I,j) Frame t+1Frame t+2Frame t

BeforeAfter

 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) 10

 Fidelity Preserving  Conventional metrics: ▪ Measure fidelity by the difference between I h & I l 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: see next page for details noised 11

 Detail Preserving  Enhanced details (edge)  Contrast preserving ▪ Human visual system is more sensitive to contrast than pixel values ▪ Gradient fields of I h & should be close,where W k is one or zero if the patch k with/o edges (canny detector) 12

 (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 13

 Proposed Measurements  A quadratic minimization problem to solve (AX = b): Contrast Similarity Detail Information(edge) Spatial-Temporal Smoothness 14

Input low = 6 -1 … … -1 Laplacian Gradient … 1 Edge Minimize Motion Estimation + + Result (X) Fidelity Bilateral filter 15

 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 for different videos, the best weight sets may be also different 16

 352 x 288 Result 17

 The proposed framework formulates noisy video super-resolution as an optimization problem, aiming to maximize the visual quality of the result  The measurements of fidelity-preserving, detail- preserving and smoothness are considered to maximize the visual quality results 18

19