가리어짐을 고려한 영상 디블러링

Abstract

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 이경무.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 국문초록 35 감사의 글 36Maste