1 research outputs found
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μμ°κ³Όνλν μ리과νλΆ,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.μ΄ μ°κ΅¬λ λ¨μ μμ°¨ 보μ λ¬Έμ λ₯Ό νκΈ° μν ν¨μ¨μ μ΄κ³ ν¨κ³Όμ μΈ λ°©λ²λ€μ μκ°νλ€. μ μλ λ°©λ²λ€μ Forward-Backward proximal splitting λ°©λ²μ κΈ°λ°ν κ²μΌλ‘ μ΄ λ°©λ²μ μ΅μ ν λ¬Έμ λ₯Ό κ²½μ¬νκ°λ²κ³Ό λ
Έμ΄μ¦ μ κ±°μ λ λ¬Έμ λ‘ λλμ΄ λ°λ³΅ λ°©λ²μ ν΅ν΄ νΌλ€. λ¨μ μμ°¨ λ¬Έμ μ μμ΄μ κ²½μ¬νκ°λ²μ ν° κ³μ° λΉμ©μ μꡬνκΈ° λλ¬Έμ μμ°¨ μ°μ°μμ μ λΉμ© ꡬν λ°©λ²μ κ°λ°νλ€. μ΄μ΄μ 6κ°μ§μ μλ‘ λ€λ₯Έ μ μΉ μ°μ°μμ κΈ°λ°ν λ
Έμ΄μ¦ μ κ±° λ°©λ²μ μ μ©ν μμ 볡μ λ°©λ²μ μ μνλ€. μ΄ μ°κ΅¬μμλ μ μλ μμ 볡μ λ°©λ²λ€μ λν μ€νμ μννλ€. μ€νμμλ μ νμ°ν¨μ (Point Spread Function)μ μ΄μ©ν΄ ν©μ±λ μμ°¨ μμμ μ΄μ©νλλ°, ν΄λΉ μ νμ°ν¨μλ νλ λμ§νΈ μΉ΄λ©λΌμ λ¨μ μμ°¨ ν¨κ³Όλ₯Ό λͺ¨λ°©ν κ²μ΄λ€.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