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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
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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.

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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