qiaochu li, qikun guo , saboya yang and jiaying liu*

Qiaochu Li, Qikun Guo, Saboya Yang and Jiaying Liu*

Institute of Computer Science and TechnologyPeking University

Scale-Compensated Nonlocal MeanSuper Resolution

2013

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Outline Introduction

Multi-frame SR Nonlocal means SR (NLM SR)

Our Algorithm Scale-detector Scale-Compensated NLM Experimental results

Conclusion & Future work

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Outline Introduction

Multi-frame SR Nonlocal means SR (NLM SR)

Our Algorithm Scale-detector Scale-Compensated NLM Experimental results

Conclusion & Future work

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Multi-Frame SR Converge low resolution images into a high

resolution image Direct motion estimation

INVALID in complex situation

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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).

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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.

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Outline Introduction

Multi-frame SR Nonlocal means SR (NLM SR)

Our Algorithm Scale-detector Scale-Compensated NLM Experimental results

Conclusion & Future work

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Scale-Detector Using SIFT descriptor to compute scales

Partial matched keypoints and the corresponding scale values.

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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)

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Scale-Compensated NLM SC NLM finds more similar patches

Comparison of unmodified and modified patch-extractor in patch matching.

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Procedures Overview of SC NLM

Scale-detector

Patch extraction

&modification

NLM SR

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Experimental Results Downsample

Blurred using 3×3 uniform mask Decimated by 3× factor Additive noise with standard deviation 2

Objective measurement Subjective measurement

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Experimental Results 3×, Objective measurement (PSNR)

Sequence NLM ARI-SWR SC-NLMForeman 31.15 30.96 31.27Tempete 22.85 22.74 23.00Text 29.23 30.06 30.11Man 27.14 27.02 27.29

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Experimental Results 3×, Subjective measurement (SSIM)

Sequence NLM ARI-SWR SC-NLMForeman 0.8109 0.8001 0.8151Tempete 0.6927 0.6737 0.7013Text 0.8592 0.8512 0.8633Man 0.7780 0.7617 0.7831

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Experimental Results

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

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Experimental Results

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

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Outline Introduction

Multi-frame SR Nonlocal means SR (NLM SR)

Our Algorithm Scale-detector Scale-Compensated NLM Experimental results

Conclusion & Future work

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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.

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Future Work More accurate scale-detector

Segmentation based scale-detector

Combination of rotation and translation-invariant algorithm Rotation-invariant measurement Translation-invariant measurement

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