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    (λ‹¨μƒ‰μˆ˜μ°¨ 보정에 λŒ€ν•œ μˆ˜ν•™μ  μ ‘κ·Ό

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    ν•™μœ„λ…Όλ¬Έ(박사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :μžμ—°κ³Όν•™λŒ€ν•™ μˆ˜λ¦¬κ³Όν•™λΆ€,2020. 2. κ°•λͺ…μ£Ό.This thesis introduces efficient and effective methods for solving monochromatic aberration correction problems. The proposed methods are based on Forward-Backward proximal splitting method, which solves the optimization problem by iteratively solving two sub parts for each step: 1. gradient descent and 2. noise removal. Since the gradient descent part has high computational cost, we develop a low-cost implementation of computing aberration operator and its transpose. Then, we propose 6 different methods, which are based on 6 types of different regularization in the noise removal part. In this thesis, we perform experiments on the proposed image restoration methods. In the experiments, we use synthetic images generated by point spread functions (PSFs), which emulate the effects of monochromatic aberration in modern digital cameras.이 μ—°κ΅¬λŠ” 단색 수차 보정 문제λ₯Ό ν’€κΈ° μœ„ν•œ 효율적이고 효과적인 방법듀을 μ†Œκ°œν•œλ‹€. μ œμ•ˆλœ 방법듀은 Forward-Backward proximal splitting 방법에 κΈ°λ°˜ν•œ κ²ƒμœΌλ‘œ 이 방법은 μ΅œμ ν™” 문제λ₯Ό κ²½μ‚¬ν•˜κ°•λ²•κ³Ό λ…Έμ΄μ¦ˆ 제거의 두 문제둜 λ‚˜λˆ„μ–΄ 반볡 방법을 톡해 ν‘Όλ‹€. 단색 수차 λ¬Έμ œμ— μžˆμ–΄μ„œ κ²½μ‚¬ν•˜κ°•λ²•μ€ 큰 계산 λΉ„μš©μ„ μš”κ΅¬ν•˜κΈ° λ•Œλ¬Έμ— 수차 μ—°μ‚°μžμ˜ μ €λΉ„μš© κ΅¬ν˜„ 방법을 κ°œλ°œν•œλ‹€. μ΄μ–΄μ„œ 6κ°€μ§€μ˜ μ„œλ‘œ λ‹€λ₯Έ μ •μΉ™ μ—°μ‚°μžμ— κΈ°λ°˜ν•œ λ…Έμ΄μ¦ˆ 제거 방법을 μ μš©ν•œ μ˜μƒ 볡원 방법을 μ œμ•ˆν•œλ‹€. 이 μ—°κ΅¬μ—μ„œλŠ” μ œμ•ˆλœ μ˜μƒ 볡원 방법듀에 λŒ€ν•œ μ‹€ν—˜μ„ μˆ˜ν–‰ν•œλ‹€. μ‹€ν—˜μ—μ„œλŠ” μ ν™•μ‚°ν•¨μˆ˜ (Point Spread Function)을 μ΄μš©ν•΄ ν•©μ„±λœ 수차 μ˜μƒμ„ μ΄μš©ν•˜λŠ”λ°, ν•΄λ‹Ή μ ν™•μ‚°ν•¨μˆ˜λŠ” ν˜„λŒ€ 디지털 μΉ΄λ©”λΌμ˜ 단색 수차 효과λ₯Ό λͺ¨λ°©ν•œ 것이닀.1 Introduction 1 2 Related Works 5 2.1 Approximation Methods 5 2.1.1 Methods 5 2.1.2 Methods Comparison and Conclusion 7 2.2 Basic Fourier Optics 8 2.2.1 Wavefront Optical Path Difference, W (x, y) 8 2.2.2 Pupil and Amplitude Transfer Functions 11 2.2.3 Point Spread Functions 12 2.3 Mathematical Preliminaries 14 2.3.1 Basic Properties of svcOperators 14 2.3.2 Regularizations in Inverse Problems 16 2.3.3 Convex Optimization Theory 21 3 Proposed Methods 30 3.1 Low Cost Implementation Using Small Support Assumption 31 3.1.1 Vectorization Techniques 33 3.2 Proposed Algorithm 34 3.2.1 Forward Backward Splitting Algorithm 35 3.2.2 Split Bregman Method 38 3.2.3 Algorithms 42 4 Experiments 47 4.1 Implementation Details 47 4.1.1 Generation of synthetic blurry images 47 4.2 Numerical Results 49 4.2.1 Synthetically Blurred Images 50 4.2.2 Image Restoration 52 5 Conclusion and Future Work 65 5.1 Conclusion 65 5.2 Future Work 66 Abstract (in Korean) 71Docto
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