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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν νλκ³Όμ κ³μ°κ³Όνμ 곡, 2021.8. λ
Ένλ―Ό.In this thesis, we focus on deep learning methods to enhance the quality of a single image. We first categorize the image quality enhancement problem into three tasks: denoising, deblurring, and super-resolution, then introduce deep learning techniques optimized for each problem. To solve these problems, we introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods that help the network extract information from high-frequency domains to reconstruct clearer images. Experiments on SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves state-of-the-art performance on each task. Furthermore, we show that our model can overcome the over-smoothing problem commonly observed in existing PSNR-oriented methods and generate more natural high-resolution images by applying adversarial training.λ³Έ νμ λ
Όλ¬Έμ λ¨μΌ μμμ νμ§ κ°νλ₯Ό μν λ₯λ¬λ κΈ°λ²μ λν μ°κ΅¬λ₯Ό λ€λ£¬λ€. μμ νμ§ κ°νλ₯Ό μμλ μ΄λ―Έμ§μ μ‘μ μ κ±° λ° λλΈλ¬λ§κ³Ό μ ν΄μλ μ΄λ―Έμ§λ₯Ό κ³ ν΄μλλ‘ λ³ννλ μ΄ν΄μλ λ¬Έμ λ‘ μΈλΆνν λ€, κ°κ°μ λ¬Έμ ν΄κ²°μ μ΅μ νλ λ₯λ¬λ κΈ°λ²μ λ¨κ³λ³λ‘ μκ°νλ€. νΉν, μμλ μμμ νΉμ±μ ν¨κ³Όμ μΌλ‘ λΆμνκ³ λ³΄λ€ κΉλν κ³ ν΄μλ μμμ μμ±νκΈ° μνμ¬ μ£Όμ΄μ§ μμμ λ€μ€ μ€μΌμΌλ‘ λΆμνλ μ¬μΈ΅ μ κ²½λ§ κ΅¬μ‘°λ₯Ό μ μνμμΌλ©°, μ΄μΈμλ λ₯λ¬λ λͺ¨λΈμ΄ μμ λ΄ λ³΅μ‘ν κ³ μ£Όνμ μμμ λν μ 보λ₯Ό ν¨κ³Όμ μΌλ‘ μΆμΆνκ³ μ¬κ±΄ν μ μλλ‘ λλ κΈ°λ²λ€μ μκ°νλ€. μ°λ¦¬λ μ μλ κΈ°λ²λ€μ SIDD, Flickr2K, DIV2K, REDS λ± λ°μ΄ν°μ
μ μ μ©νμ¬ κΈ°μ‘΄μ λ₯λ¬λ κΈ°λ° κΈ°λ²λ³΄λ€ ν₯μλ μ±λ₯μ μ€νμ μΌλ‘ μ¦λͺ
νμλ€. λν μ΄ν΄μλ λ¬Έμ ν΄κ²°μ μν΄ νμ΅λ μ¬μΈ΅ μ κ²½λ§μ μΆκ°μ μΈ μ λμ νμ΅μ μ μ©ν¨μΌλ‘μ¨ κΈ°μ‘΄ λ₯λ¬λ κΈ°λ²λ€μ νκ³λ‘ μ§μ λμλ λΆλΆ νκ· ν λ¬Έμ λ₯Ό 극볡νκ³ λ³΄λ€ μμ°μ€λ¬μ΄ κ³ ν΄μλ μμμ μμ±ν μ μμμ 보μλ€.1. Introduction 1
2. Preliminaries 4
2.1 Image Denoising 4
2.1.1 Problem Formulation: AWGN 4
2.1.2 Existing Methods 6
2.2 Image Deblurring 7
2.2.1 Problem Formulation: Blind Deblur 7
2.2.2 Existing Methods 7
2.3 Single Image Super-Resolution 9
2.3.1 Problem Formulation: SISR 9
2.3.2 Existing Methods 12
3. Image Denoising 15
3.1 Proposed Methods 15
3.1.1 Multi-scale Edge Filtering 15
3.1.2 Feature Attention Module 17
3.1.3 Network Architecture 19
3.2 Experiments 21
3.2.1 Training Details 21
3.2.2 Experimental Results on DIV2K+AWGN dataset 21
3.2.3 Experimental Results on SIDD dataset 26
4. Image Deblurring 28
4.1 Proposed Methods 28
4.1.1 Multi-Scale Feature Analysis 29
4.1.2 Network Architecture 29
4.2 Experiments 31
4.2.1 Training Details 31
4.2.2 Experimental Results on Flickr2K dataset 31
4.2.3 Experimental Results on REDS dataset 34
5. Single Image Super-Resolution 38
5.1 Proposed Methods 38
5.1.1 High-Pass Filtering Loss 39
5.1.2 Gradient Magnitude Similarity Map Masking 41
5.1.3 Soft Gradient Magnitude Similarity Map Masking 43
5.1.4 Network Architecture 44
5.1.5 Adversarial Training for Perceptual Generative Model 45
5.2 Experiments 47
5.2.1 Training Details 47
5.2.2 Experimental Results on DIV2K dataset 48
5.2.3 Experimental Results on Set5/Set14 dataset 55
5.2.4 Experimental Results on REDS dataset 60
6. Conclusion and Future Works 63λ°
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