学位论文 > 优秀研究生学位论文题录展示
Super Resolution Imaging and It\'s Applications
作 者: Shahryar Shafique Qureshi
导 师: 李学明
学 校: 北京邮电大学
专 业: 信号与信息处理
关键词: Super resolution Image Quality Metrics Object Recognition MobileCloud Computing POCS IBP
分类号: TP391.41
类 型: 博士论文
年 份: 2012年
下 载: 114次
引 用: 0次
阅 读: 论文下载
内容摘要
Images with higher resolution are required in almost all digital imaging applications. For past few decades considerable advancement has been realized in imaging devices. Modern digital cameras are equipped with high quality lenses, increased pixel resolution as well as small size, but still they are far from the perfection because of being costly and having physical limitations of hardware for example sensor, lens and optics. Using signal processing techniques to generate a high resolution image from a low resolution image or a set of low resolution images is a cheaper and effective alternative. Such kind of resolution enhancement is called super resolution image reconstruction. The resolution enhancement is achieved by fractional-pixel displacements between observed low resolution images. Super resolution methods tend to overcome the limitations of an imaging system where there is no need for additional hardware. This fact has made super resolution a hottest research area both scientifically and commercially. The research work in this thesis is mainly focused on four tasks.Firstly, we present an overview of well-known super resolution techniques for both multi-image super resolution and single-image super resolution. The performance comparison and analysis are the main concerns to see the advantages and disadvantages of multi-image super resolution techniques. After applying the super resolution techniques to our tested synthetic image data we critic the affect of the full reference quality metrics (peak signal-to-noise ratio and mean square error). Secondly, an optimized approach is introduced for the effective selection of low resolution images in the process of super resolution image reconstruction. We used minimum two low resolution images and performed experiments to validate our methodology.Thirdly, we investigate the non-parametric super resolutions for object recognition in Radar. We analyze how super resolution algorithm can be useful for resolving two or more targets in radar imaging system. The performed simulation results help us to evaluate the performance of non-parametric super resolution algorithms discussed. Finally, a little research has been carried out about a proposal for futuristic design of implementing super resolution image reconstruction in the environment of mobile cloud computing. The proposed framework is based on the idea of utilizing the cloud computing infrastructure through the mobile device+mobile web (or internet service provider) as an Educational Tool for super resolution image reconstruction.Throughout this research work, experiments on various real and synthetic image data are conducted to validate and evaluate the performance of the existing and proposed approaches.
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全文目录
Abstract 6-8 Dedication 8-9 TABLE OF CONTENTS 9-13 List of Figures 13-15 List of Tables 15-16 List of Abbreviations 16-19 CHAPTER 1 INTRODUCTION 19-31 1.1 THE DEFINITION OF RESOLUTIGN 20-21 1.1.1 Pixel Resolution 20 1.1.2 Spatial Resolution 20-21 1.1.3 Brightness Resolution 21 1.1.4 Temporal Resolution 21 1.1.5 Spectral Resolution 21 1.2 MOTIVATION FOR USING SUPER RESOLUTION METHODS 21-23 1.2.1 Different Ways Resolution Enhancement and Limits on Them 22-23 1.2.1.1 Hardware Solution 22-23 1.2.1.2 Software Solution 23 1.3 DEFINITION OF SUPER RESOLUTION 23-28 1.3.1 What is Super Resolution? 24-26 1.3.2 Applications of Super Resolution 26-27 1.3.3 3 Key Concepts about Super Resolution 27-28 1.4 AUTHOR'S CONTRIBUTION 28-29 1.5 ORGANIZATION OF THESIS 29-30 REFERENCES 30-31 CHAPTER 2 INTRODUCTION TO IMAGE SUPER RESOLUTION 31-76 2.1 INTRODUCTION 31-32 2.2 LOW RESOLUTION INPUT IMAGES 32-33 2.3 OBSERVATION MODEL FOR SUPER RESOLUTION IMAGE 33-35 2.4 MULTI-IMAGE SR TECHNIOUES 35-46 2.4.1 Frequeney Domain Techniques 36-39 2.4.1.1 Alias Removal Reconstruction Methods 36-37 2.4.1.2 Recursive Least Square Method 37-38 2.4.1.3 Recursive Total Least Square Method 38 2.4.1.4 Two Phase SR Approach by Tom and Katsaggelos 38 2.4.1.5 Wavelet Based SR reconstruction Models 38-39 2.4.2 Spatial Domain Techniques 39-46 2.4.2.1 Non-uniform Interpolation 39-40 2.4.2.2 Projection onto Convex Sets(POCS) 40-42 2.4.2.3 Iterative Back-projection(IBP) 42-43 2.4.2.4 Maximum A-posteriori(MAP) 43-44 2.4.2.5 MAP/ML-POCS Hybrid Super resolution 44-45 2.4.2.6 Optimal and Adaptive Filtering SR Technique 45-46 2.4.2.7 Tikhonov-Arsenin Regularization Method 46 2.5 SINGLE IMAGE SUPER RESOLUTION TECHNIOUES 46-55 2.5.1 Freeman et al.fast NN-based Method 47-52 2.5.1.1 Training Set Generation 48-49 2.5.1.2 Markov Network Algorithm 49-50 2.5.1.3 One Pass Algorithm 50-52 2.5.2 Chang et al.LLE Method 52-54 2.5.4 Super Resolution from Sparse Representation ofpatches ofLR Images 54-55 2.6 EXPERIMENTS AND SIMULATION RESULTS 55-57 2.7 QUALITY ASSESSMENT METRICS FOR SUPER RESOLUTION OF IMAGES 57-67 2.7.1 Subjectivc Test Methods 57-58 2.7.2 Objective Test Methods 58-59 2.7.2.1 Full Reference Quality Assessment Metrics 58-59 2.7.3 Our Methodology 59-60 2.7.4 Quality Metric Based Comparison of SR Techniques 60-61 2.7.5 Noise Effect on Image Quality 61-65 2.7.6 Number of Images Effect on the Image Quality 65-66 2.7.7 Effect of Number of Iterations on Image Quality 66-67 2.8 SUMMARY 67-68 REFERENCES 68-76 CHAPTER 3 ADEQUATE AND REALISTIC STRATEGY FOR SUPER RESOLUTION IMAGING 76-85 3.1 INTRODUCTION 76 3.2 RELATED WORK 76-77 3.3 PROPOSED METHOD 77-82 3.3.1 Key Steps of Our Method 78-82 3.4 SIMULATION RESULTS 82-83 3.5 SUMMARY 83-84 REFERENCES 84-85 CHAPTER 4 APPLYING NON-PARAMETRIC SUPER RESOLUTION TECHNIQUES IN RADAR FOROBJECT RECOGNITION 85-100 4.1 INTRODUCTION 85-86 4.2 RADAR 86-87 4.3 SIGNAL-TO-NOISE RATIO IN A RADAR SYSTEM 87 4.4 RADAR RESOLUTION 87-90 4.4.1 Range Resolution 88-89 4.4.2 Angular Resolution 89 4.4.3 Doppler Resolution 89-90 4.5 RADAR POINT SPREAD FUNCTION 90-91 4.6 OBJECT RECOGNITION USING SUPER RESOLUTION TECHNIQUES 91-95 4.6.1 Imaging Model 92 4.6.2 Super Resolution Algorithms 92-95 4.6.2.1 Matrix Inverse 93 4.6.2.2 Minimum Mean Square Error(MMSE) 93-94 4.6.2.3 Singular Value Decomposition(SVD) 94-95 4.7 COMPARISON AND SIMULATION RESULTS 95-97 4.8 SUMMARY 97 REFERENCES 97-100 CHAPTER 5 A PROPOSED FUTURISTIC FRAMEWORK FOR IMAGE SUPER RESOLUTION IN THEMOBILE CLOUD COMPUTING PLATFORM 100-118 5.1 INTRODUCTION 100-101 5.1.1 Why we choose Mobile Cloud Computing 100-101 5.2 CLOUD COMPUTING 101-102 5.3 MOBILE CLOUD COMPUTING 102-107 5.3.1 What is Mobile Cloud Computing? 103 5.3.2 Architecture of MCC 103-105 5.3.2.1 Mobile Devices 104 5.3.2.2 Mobile Networks/Network Operators 104-105 5.3.2.3 Internet Service Provider(ISP) 105 5.3.2.4 Cloud Computing Infrastructure 105 5.3.3 Application of MCC 105-106 5.3.4 Advantages of Mobile Cloud Computing 106-107 5.3.4.1 Larger Storage Capacity and Effective sharing of Data 106 5.3.4.2 Save Battery Lifetime 106 5.3.4.3 Improved Security and Reliability 106-107 5.3.4.4 Network Bandwidth and Latency 107 5.3.4.5 Network Availability and Intermittency 107 5.4 RELATED WORK 107-109 5.4.1 Mobile Image Processing 107-109 5.5 PROPOSED FRAMEWORK 109-113 5.5.1 Research Motivation 109 5.5.2 Research Method 109-112 5.5.3 Experiment Performed and its Results 112-113 5.6 SUMMARY 113-114 REFERENCES 114-118 CHAPTER 6 CONCLUSION AND FUTURE WORK 118-122 6.1 RESEARCH SUMMARY 118-120 6.1.1 Super Resolution 118-119 6.1.2 Target Recognition 119 6.1.3 A Proposed Futuristic Framework 119-120 6.2 RECOMMENDATIONS FOR FUTURE RESEARCH 120-122 LIST OF PUBLICATIONS 122-123 ACKNOWLEDGEMENTS 123-124
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