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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭斯彥 | zh_TW |
| dc.contributor.advisor | Sy-Yen Kuo | en |
| dc.contributor.author | 陳韋廷 | zh_TW |
| dc.contributor.author | Wei-Ting Chen | en |
| dc.date.accessioned | 2024-04-10T16:13:06Z | - |
| dc.date.available | 2024-04-11 | - |
| dc.date.copyright | 2024-04-10 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-04-08 | - |
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IEEE transactions on image processing, 24(11):3522–3533, 2015. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92548 | - |
| dc.description.abstract | 在數位時代中，對於自動駕駛、醫療診斷等多種應用領域，高品質視覺數據的需求顯得尤為重要。然而，由於惡劣天氣、低光照、模糊及噪聲等因素，真實環境中捕獲的影像常常受損，這不僅妨礙了人類的判斷能力，也減弱了機器學習模型在進行物體檢測、語義分割等下游任務的效能。面對這些挑戰，從精準的影像質量評估開始，到運用先進的影像恢復技術變得至關重要。隨著深度學習技術的進步，我們現在有能力利用深度學習模型從數據中學習複雜的模式和特徵，自動檢測和修正影像質量問題，達到顯著的進步。
本研究通過深度學習技術，針對受損影像系統性地開發了從影像質量評估到二維影像及三維體積重建的相關應用。此過程涵蓋了從初步的影像質量評估，到二維影像的質量提升，進而到三維體積的重建技術，並最終探討了如何從機器的視角出發，對受損影像進行處理，以提升下游機器學習任務的性能。透過一系列廣泛的實驗，本研究不僅在多種受損情況下顯著提升了影像質量，也展示了我們的系統如何直接增強下游任務的性能，為複雜環境中高品質影像處理和分析提供了新的見解和工具。 | zh_TW |
| dc.description.abstract | In the digital era, the demand for high-quality visual data has become increasingly critical for a variety of applications, including autonomous driving and medical diagnostics. However, images captured in real-world conditions often suffer from degradation due to adverse weather, low lighting, blurriness, and noise. These factors not only hinder human judgment but also impair the performance of machine learning models in tasks such as object detection and semantic segmentation. Addressing these challenges, starting with precise image quality assessment to employing advanced image restoration techniques, has become essential. With advancements in deep learning technology, we now have the capability to use deep learning models to learn complex patterns and features from data, automatically detecting and correcting image quality issues for significant improvements.
This study systematically develops applications ranging from image quality assessment to the restoration of two-dimensional images and the reconstruction of three-dimensional volumes using deep learning technology for degraded images. The process encompasses initial image quality assessment, enhancement of two-dimensional image quality, and then reconstruction of three-dimensional volumes. It further explores how to process damaged images from a machine-centric perspective to enhance the performance of downstream machine learning tasks. Through a series of extensive experiments, this research not only significantly improves image quality under various degraded conditions but also demonstrates how our system directly enhances the performance of downstream tasks, providing new insights and tools for high-quality image processing and analysis in complex environments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-04-10T16:13:06Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-04-10T16:13:06Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 iii
Abstract v Contents vii List of Figures xiii List of Tables xxi Chapter 1 Introduction 1 1.1 Image Quality Assessment 3 1.2 2D Image Restoration 6 1.2.1 Single Image Haze Removal 8 1.2.2 Single Image Snow Removal 13 1.3 3D Volume Reconstruction 16 1.4 Improve Machine Vision under Degraded Scenes 20 Chapter 2 Related work 25 2.1 Image Quality Assessment 25 2.1.1 Quality Assessment of Face Images 25 2.1.2 General Image Quality Assessment 26 2.1.3 Face Landmark Detection 27 2.2 2D Image Restoration 28 2.2.1 Single Image Haze Removal 28 2.2.2 Single Image Snow Removal 31 2.3 Generative Adversarial Networks (GANs) 33 2.4 3D Volume Reconstruction 33 2.4.1 3D Scene Reconstruction with NeRF 33 2.4.2 Estimating Scattering Coefficients 34 2.4.3 Seeing Through Scattering Media 34 2.5 Enhancing Machine Vision in Degraded Conditions 35 2.5.1 Highlevel Vision Tasks in Degraded Scenarios 35 2.5.2 Segment Anything Model (SAM) 36 Chapter 3 Generic Face Image Quality Assessment 37 3.1 Proposed Method 38 3.1.1 Model Overview 38 3.1.2 SelfSupervised DualSet Degradation Representation Learning 40 3.1.2.1 Patchbased Degradation Learning 40 3.1.2.2 Our Solution 41 3.1.3 Landmarkguided GFIQA 44 3.1.4 Loss Functions 46 3.2 Experimental Results 47 3.2.1 Experiment Settings 47 3.2.2 Training Details 49 3.2.3 Performance Evaluation 52 3.2.4 Ablation Study 52 3.3 Discussion 56 Chapter 4 2D Image Restoration Haze Removal 59 4.1 Dark Channel Prior Limitations 59 4.2 Patch Map 61 4.3 Patch Map Selection Net 63 4.4 Patch Map Based Hybrid Learning DehazeNet 66 4.5 Hybrid Learning DehazeNetwork 74 4.6 Experimental Result 79 4.6.1 Dataset and Training Detail 79 4.6.2 Model Complexity Analysis 81 4.6.3 Quantitative Analysis on the Synthetic Dataset 82 4.6.4 Dehazed Results on Real World Images 85 4.6.5 Ablation Study 90 4.6.6 Analysis on Learned Latent Statistical Regularities 94 4.6.7 Analysis on Maximal Patch Size Selection 96 4.6.8 Limitation 98 Chapter 5 2D Image Restoration Snow Removal 99 5.1 Modified Snow Model Formulation 100 5.2 Joint Size and TransparencyAware Snow Removal 101 5.2.1 Architecture of Snow Removal 101 5.2.2 Veiling Effect Removal 108 5.3 Experimental Result 111 5.3.1 Dataset Generation 111 5.3.2 Training Details 112 5.3.3 Comparison with Stateoftheart Methods 113 5.3.4 Ablation Study 117 Chapter 6 3D Volume Reconstruction 119 6.1 Method 120 6.1.1 Preliminary on Neural Radiance Fields 120 6.1.2 3D Haze Formation 120 6.1.3 Hazeaware Neural Radiance Field 122 6.1.4 Implementation Details 127 6.2 Implementation Details 128 6.3 Experiments with Synthetic Scenes 131 6.3.1 Synthetic Data Generation 131 6.3.2 Comparative Analysis 132 6.3.3 Ablation Study 135 6.4 Experimentally Captured Results 136 6.4.1 Data Collection 136 6.4.2 Implementation Details 137 6.4.3 Comparison 138 6.5 Discussion 139 Chapter 7 Improve Machine Vision under Degraded Scenes 143 7.1 Proposed Method 144 7.1.1 Preliminary: Segment Anything Model (SAM) 144 7.1.2 Robust Segment Anything Model (RobustSAM) 144 7.1.2.1 Model Overview 145 7.1.2.2 AntiDegradation Mask Feature Generation 147 7.1.2.3 AntiDegradation Output Token Generation 149 7.1.2.4 Overall Loss 150 7.2 Implementation Details 150 7.2.1 Dataset for Training and Evaluation of RobustSAM 150 7.2.2 Training Detail 151 7.2.3 Evaluation Protocol 152 7.3 Experimental Results 153 7.3.1 Performance Evaluation 155 7.3.2 Ablation Study 157 7.3.3 Different Backbones in RobustSAM 160 7.3.4 Token Visualization 160 7.3.5 Improving SAMprior Tasks 161 Chapter 8 Conclusion 163 Chapter 9 Limitation and Future Work 165 References 167 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 二維影像還原 | zh_TW |
| dc.subject | 影像質量評估 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 強健的電腦視覺系統 | zh_TW |
| dc.subject | 三維體積重建 | zh_TW |
| dc.subject | 3D Volume Reconstruction | en |
| dc.subject | Robust Computer Vision System | en |
| dc.subject | Deep Learning | en |
| dc.subject | 2D Image restoration | en |
| dc.subject | Image Quality Assessment | en |
| dc.title | 劣化場景下的視覺研究 | zh_TW |
| dc.title | Vision in Degraded Scenes | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 雷欽隆;顏嗣鈞;丁建均;黃彥男;陳俊良;游家牧;林宗男 | zh_TW |
| dc.contributor.oralexamcommittee | Chin-Laung Lei;Hsu-Chun Yen;Jian-Jiun Ding;Yen-Nun Huang;Jiann-Liang Chen;Chia-Mu Yu;Tsung-Nan Lin | en |
| dc.subject.keyword | 影像質量評估,二維影像還原,三維體積重建,強健的電腦視覺系統,深度學習, | zh_TW |
| dc.subject.keyword | Image Quality Assessment,2D Image restoration,3D Volume Reconstruction,Robust Computer Vision System,Deep Learning, | en |
| dc.relation.page | 206 | - |
| dc.identifier.doi | 10.6342/NTU202400832 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-04-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電子工程學研究所 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
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