11 research outputs found
๊ฐ๋ฆฌ์ด์ง์ ๊ณ ๋ คํ ์์ ๋๋ธ๋ฌ๋ง
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2014. 2. ์ด๊ฒฝ๋ฌด.In this thesis, a novel blur model that can deal with occlusion in the blurred image from a scene with depth discontinuities is proposed. Existing deblurring methods usually ignore the occlusion that occurs near the depth variations but it causes severe artifacts near the object boundary, which is a critical factor in deblurring. Based on the analysis about the blur kernel near the depth discontinuities for a two-layer image model, a new occlusion-aware blur model which can make use of the information of occluded regions is proposed. Proposed model jointly recovers the depth map, foreground mask and restored image with accurate object boundary from two blurred observations. Also, a highly accurate optimization method is provided based on MCMC. Comparative experimental results on synthetic and real blurred images demonstrate convincingly that proposed model gives satisfactory results.Abstract i
Contents ii
List of Figures v
List of Tables vii
1 Introduction 1
1.1 Background and Research Issues . . . . . . . . . . . . . . . . . . . . 1
1.2 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Related work 4
2.1 Uniform Blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Non-Uniform Blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 Non-Uniform Blur from Camera Motion . . . . . . . . . . . . 5
2.2.2 Non-Uniform Blur with Depth Variations . . . . . . . . . . . 5
2.2.3 Non-Uniform Blur with Occlusions . . . . . . . . . . . . . . . 5
2.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Analysis of Occlusion during Camera Motion 7
3.1 The Two-Layer Model of Latent Image . . . . . . . . . . . . . . . . . 7
3.2 The Two-Layer Image Transformation . . . . . . . . . . . . . . . . . 9
3.3 Occlusion-Aware Blur Model . . . . . . . . . . . . . . . . . . . . . . 10
4 Occlusion-Aware Motion Deblurring 14
4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Camera Pose Interpolation . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.4 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.4.1 Markov chain Monte Carlo . . . . . . . . . . . . . . . . . . . 18
5 Discussion 21
6 Experiments 22
7 Conclusion 29
7.1 Summary of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 29
7.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
7.2.1 Multi Layer Scenes . . . . . . . . . . . . . . . . . . . . . . . . 30
7.2.2 Projective Motion . . . . . . . . . . . . . . . . . . . . . . . . 30
7.2.3 Dynamic Scenes . . . . . . . . . . . . . . . . . . . . . . . . . 31
7.2.4 Real-Time Deblurring and 3D Reconstruction . . . . . . . . . 31
Bibliography 32
๊ตญ๋ฌธ์ด๋ก 35
๊ฐ์ฌ์ ๊ธ 36Maste
ๅฐๅๅฎไฝ ๆ่ก้็ผไบๆฅญ์ ๊ดํ ็ก็ฉถ : ๅฐๅๅๅ็ก็ฉถ์ผํฐ ่ฒๆไบๆฅญ์ ไธญๅฟ์ผ๋ก
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ํ์ ๋ํ์ :ํ์ ํ๊ณผ ์ ์ฑ
ํ์ ๊ณต,2000.Maste