Although significant progress has been made in imaging devices during the past few decades, the photographs acquired by digital cameras are still far from perfection due to the physical limitations of hardware such as aperture, lens and sensor. This fact brings out the demand for study on image enhancement: a computational technique that aims to improve the interpretability or perception of information in photographs for human viewers. The work in this thesis mainly focuses on three tasks in image enhancement. / Finally, since the radiance of the real world spans several orders of magnitude and its dynamic range dramatically exceeds the capability of the current digital cameras, there often exist some undesirable over- or under-exposed regions in a photograph. The third part of this thesis aims at producing one great looking well-exposed image that is virtually impossible with a single exposure by compositing a stack of photos at different exposures taken with a conventional camera. Particularly, a simple but effective method is presented to describe how to take advantage of the gradient information to accomplish exposure composition in both static and dynamic scenes. Compared to conventional high dynamic range (HDR) imaging work, the proposed approach is quite appealing in practice since it is computationally efficient and easy to use, and frees users from the tedious radiometric calibration and tone mapping steps. / Firstly, since the camera sensor has limited resolution, the acquired images cannot capture the scene very detailedly. Hence, people often resort to a postprocessing technique called super-resolution (SR) to enhance the resolution of the captured images. In the first part of this thesis, two approaches are presented to address the challenging single image SR problem, which is to recover a high-resolution (HR) image from one low-resolution (LR) input. Specifically, a novel learning-based framework is designed specifically for face image SR task from the perspective of DCT domain. In addition, an efficient two-step scheme is developed to super-resolve generic image by exploiting the salient edges of the input LR image. / Secondly, due to the limitation of lens and aperture, some cameras cannot produce pleasant photographs with desired focus setting. For example, portrait photography that requires shallow depth of field (DOF) is not allowed when using the compact point-and-shoot cameras. In the second part of this thesis, a new and complete postprocessing-based focus editing system that is able to handle the tasks of focus map estimation, image refocusing and defocusing, is developed to overcome the optical limitations and create different kinds of novel photos with desired focus setting from an imperfect photo. / Throughout this work, extensive experiments on various real and synthetic image data are conducted to evaluate the performance of the proposed algorithms. / Zhang, Wei. / Adviser: Wai-Kuen Chan. / Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 116-125). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

by Wen Jiang. / c.2 author's name on frame header: Wen, Jiang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 105-[113]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Hung, Chun Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 58-63). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Generalized Photometric Stereo --- p.6 / Chapter 2.1 --- Problem Description --- p.6 / Chapter 2.2 --- Related Work --- p.9 / Chapter 2.3 --- Photometric Stereo with Environment Lighting --- p.11 / Chapter 2.4 --- Estimating Surface Normals --- p.13 / Chapter 2.4.1 --- Surface Normal and Albedo Estimation --- p.14 / Chapter 2.5 --- Data Acquisition Configuration --- p.17 / Chapter 2.6 --- Issues --- p.19 / Chapter 2.7 --- Outlier Removal --- p.22 / Chapter 2.8 --- Experimental Results --- p.23 / Chapter 3 --- Generalized Stereo Matching --- p.30 / Chapter 3.1 --- Problem Description --- p.30 / Chapter 3.2 --- Related Work --- p.32 / Chapter 3.3 --- Our Approach --- p.33 / Chapter 3.3.1 --- Notations and Problem Introduction --- p.33 / Chapter 3.3.2 --- Depth and Motion Initialization --- p.35 / Chapter 3.3.3 --- Volume-based Structure Prior --- p.38 / Chapter 3.3.4 --- Objective Function with Volume-based Priors --- p.43 / Chapter 3.3.5 --- Numerical Solution --- p.46 / Chapter 3.4 --- Results --- p.48 / Chapter 4 --- Conclusion --- p.56 / Bibliography --- p.57

隨著數碼相機與移動照相設備的日益普及，現時的拍攝照片數量遠遠超過以前。數碼照相機的內在缺陷使得數字圖像還原領域得到廣泛的興趣。在本論文中，我們將研究圖像去模糊。圖像去模糊旨在從一張模糊的圖像恢復出清晰的圖像。它是一個在計算機視覺和圖形學有理論和實踐影響力的根本問題。單圖反卷積問題是一個十分挑戰的問題因為我們觀察到的信息比要恢復的信息要少。我們討論模糊核估計並分析為什麼現存的算法可以獲得成功。基於這些分析和理解，我們提出了一個創新的統一框架。該框架具有優異的圖像對模糊性能，並且只需使用很少的運算時間。這個框架還被擴展到了非均一的圖像去模糊上，並且取得與最先進算法相當的效果。 / 在現實模糊圖像中，模糊常常是非均一的，這種模糊具有更大的挑戰性。均一模糊的技術發展使得這個問題相對於以前較容易著手。在本論文中，我們對現存的相機抖動模型進行了詳細的研究並討論其中存在的一些問題。我們對相機模型進行歸納總結並且提出了基於每個平面的非均一圖像去模糊框架。基於這個框架，我們解決了一種特殊形式的模糊。這種模糊是產生於外平面運動，常見於用車載，體育和監控相機拍攝的照片。我們在具有挑戰性的網絡圖片和自己拍攝的圖片上進行測試，驗證了我們的方法的正確性。 / With the popularity of digital cameras and mobile phone cameras, much more photos are being taken nowadays than ever before. The imperfection of digital cameras arouses broad interest in digital image restoration. In this thesis, we study an important topic, i.e., image deblurring, which aims to recover a sharp image from only a blurry observation. It is one of the fundamental problems in computer vision and graphics with both theoretical and practical impact. Single image blind deconvolution is challenging since there are more unknowns than observations. We discuss problems involving blur kernel estimation and why state-ofthe-art methods work. These insights lead to a novel unified framework to achieve decent deblurring performance on publicly available datasets in faster speed. The extension of the framework to non-uniform image deblurring also achieves comparable performance to state-of-the-art methods. / Further, in real blurred images, it is quite often that blur is spatiallyvariant, which is very difficult to deal with. Advance in uniform deblurring makes this problem tractable. We make a detailed study of current camera shake models and discuss problems in these models. We also generalize the framework and propose a plane-wise non-uniform image deblurring framework. Based on it, we tackle a specific type of blur involving out-of-plane motion, which typically appears on photos captured using car, sport and surveillance camera. We validate our method on challenging photos obtained from internet and taken by ourselves. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zheng, Shicheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 71-79). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Contributions --- p.5 / Chapter 1.3 --- Thesis Outline --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Non-blind Image Deconvolution --- p.8 / Chapter 2.2 --- Blind Deconvolution --- p.13 / Chapter 2.3 --- Non-uniform Image Deblurring --- p.14 / Chapter 3 --- Unnatural Representation For Natural Image Deblurring --- p.19 / Chapter 3.0.1 --- Analysis --- p.21 / Chapter 3.0.2 --- Our Contribution --- p.23 / Chapter 3.1 --- Framework --- p.24 / Chapter 3.2 --- Optimization --- p.28 / Chapter 3.2.1 --- Solve for k --- p.28 / Chapter 3.2.2 --- Solve for k{U+1D57}⁺¹ with l{U+1D57}+1 --- p.32 / Chapter 3.2.3 --- Final Image Restoration --- p.34 / Chapter 3.3 --- Discussion --- p.34 / Chapter 3.4 --- Experimental Results --- p.38 / Chapter 3.5 --- Concluding Remarks --- p.41 / Chapter 4 --- Forward Motion Deblurring --- p.43 / Chapter 4.1 --- Background --- p.45 / Chapter 4.2 --- OurModel --- p.51 / Chapter 4.3 --- Forward Motion Deblurring. --- p.55 / Chapter 4.3.1 --- Kernel and Image Restoration --- p.55 / Chapter 4.4 --- Implementation and Discussion --- p.58 / Chapter 4.5 --- Experimental Results --- p.59 / Chapter 4.6 --- Conclusion and Limitation --- p.64 / Chapter 5 --- Conclusion --- p.65 / Chapter A --- New Sparsity Function --- p.67 / Bibliography --- p.71

by Kin-wai Mak. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 52-54). / Chapter 1 --- Introduction to Parallel Computing --- p.7 / Chapter 1.1 --- Parallel Computer Models --- p.8 / Chapter 1.2 --- Forms of Parallelism --- p.12 / Chapter 1.3 --- Performance Evaluation --- p.15 / Chapter 1.3.1 --- Finding Machine Parameters --- p.15 / Chapter 1.3.2 --- Amdahl's Law --- p.19 / Chapter 1.3.3 --- Gustafson's Law --- p.20 / Chapter 1.3.4 --- Scalability Analysis --- p.20 / Chapter 2 --- Introduction to Image Processing --- p.26 / Chapter 2.1 --- Image Restoration Problem --- p.26 / Chapter 2.1.1 --- Toeplitz Least Squares Problems --- p.29 / Chapter 2.1.2 --- The Need For Regularization --- p.31 / Chapter 2.1.3 --- Guide Star Image --- p.32 / Chapter 3 --- Toeplitz Solvers --- p.34 / Chapter 3.1 --- Introduction --- p.34 / Chapter 3.2 --- Parallel Implementation --- p.38 / Chapter 3.2.1 --- Overview of MasPar --- p.38 / Chapter 3.2.2 --- Design Methodology --- p.39 / Chapter 3.2.3 --- Implementation Details --- p.42 / Chapter 3.2.4 --- Application to Ground Based Astronomy --- p.44 / Chapter 3.2.5 --- Performance Analysis --- p.46 / Chapter 3.2.6 --- The Graphical Interface --- p.48 / Bibliography

by Chen Zhi-Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references. / List of Symbols S --- p.1 / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Active Vision Paradigm and Calibration of Active Vision System --- p.1.1 / Chapter 1.1.1 --- Active Vision Paradigm --- p.1.1 / Chapter 1.1.2 --- A Review of the Existing Active Vision Systems --- p.1.1 / Chapter 1.1.3 --- A Brief Introduction to Our Active Vision System --- p.1.2 / Chapter 1.1.4 --- The Stages of Calibrating an Active Vision System --- p.1.3 / Chapter 1.2 --- Projective Invariants and Their Applications to Feature Tracking --- p.1.4 / Chapter 1.3 --- Thesis Overview --- p.1.4 / References --- p.1.5 / Chapter Chapter 2 --- Calibration for an Active Vision System: Camera Calibration / Chapter 2.1 --- An Overview of Camera Calibration --- p.2.1 / Chapter 2.2 --- Tsai's RAC Based Camera Calibration Method --- p.2.5 / Chapter 2.2.1 --- The Pinhole Camera Model with Radial Distortion --- p.2.7 / Chapter 2.2.2 --- Calibrating a Camera Using Mono view Noncoplanar Points --- p.2.10 / Chapter 2.3 --- Reg Willson's Implementation of R. Y. Tsai's RAC Based Camera Calibration Algorithm --- p.2.15 / Chapter 2.4 --- Experimental Setup and Procedures --- p.2.20 / Chapter 2.5 --- Experimental Results --- p.2.23 / Chapter 2.6 --- Conclusion --- p.2.28 / References --- p.2.29 / Chapter Chapter 3 --- Calibration for an Active Vision System: Head-Eye Calibration / Chapter 3.1 --- Why Head-Eye Calibration --- p.3.1 / Chapter 3.2 --- Review of the Existing Head-Eye Calibration Algorithms --- p.3.1 / Chapter 3.2.1 --- Category I Classic Approaches --- p.3.1 / Chapter 3.2.2 --- Category II Self-Calibration Techniques --- p.3.2 / Chapter 3.3 --- R.Tsai's Approach for Hand-Eye (Head-Eye) Calibration --- p.3.3 / Chapter 3.3.1 --- Introduction --- p.3.3 / Chapter 3.3.2 --- Definitions of Coordinate Frames and Homogeoeous Transformation Matrices --- p.3.3 / Chapter 3.3.3 --- Formulation of the Head-Eye Calibration Problem --- p.3.6 / Chapter 3.3.4 --- Using Principal Vector to Represent Rotation Transformation Matrix --- p.3.7 / Chapter 3.3.5 --- Calculating R cg and Tcg --- p.3.9 / Chapter 3.4 --- Our Local Implementation of Tsai's Head Eye Calibration Algorithm --- p.3.14 / Chapter 3.4.1 --- Using Denavit - Hartternberg's Approach to Establish a Body-Attached Coordinate Frame for Each Link of the Manipulator --- p.3.16 / Chapter 3.5 --- Function of Procedures and Formats of Data Files --- p.3.23 / Chapter 3.6 --- Experimental Results --- p.3.26 / Chapter 3.7 --- Discussion --- p.3.45 / Chapter 3.8 --- Conclusion --- p.3.46 / References --- p.3.47 / Appendix I Procedures --- p.3.48 / Chapter Chapter 4 --- A New Tracking Method for Shape from Motion Using an Active Vision System / Chapter 4.1 --- Introduction --- p.4.1 / Chapter 4.2 --- A New Tracking Method --- p.4.1 / Chapter 4.2.1 --- Our approach --- p.4.1 / Chapter 4.2.2 --- Using an Active Vision System to Track the Projective Basis Across Image Sequence --- p.4.2 / Chapter 4.2.3 --- Using Projective Invariants to Track the Remaining Feature Points --- p.4.2 / Chapter 4.3 --- Using Factorisation Method to Recover Shape from Motion --- p.4.11 / Chapter 4.4 --- Discussion and Future Research --- p.4.31 / References --- p.4.32 / Chapter Chapter 5 --- Experiments on Feature Tracking with 3D Projective Invariants / Chapter 5.1 --- 8-point Projective Invariant --- p.5.1 / Chapter 5.2 --- Projective Invariant Based Tranfer between Distinct Views of a 3-D Scene --- p.5.4 / Chapter 5.3 --- Transfer Experiments on the Image Sequence of an Calibration Block --- p.5.6 / Chapter 5.3.1 --- Experiment 1. Real Image Sequence 1 of a Camera Calibration Block --- p.5.6 / Chapter 5.3.2 --- Experiment 2. Real Image Sequence 2 of a Camera Calibration Block --- p.5.15 / Chapter 5.3.3 --- Experiment 3. Real Image Sequence 3 of a Camera Calibration Block --- p.5.22 / Chapter 5.3.4 --- Experiment 4. Synthetic Image Sequence of a Camera Calibration Block --- p.5.27 / Chapter 5.3.5 --- Discussions on the Experimental Results --- p.5.32 / Chapter 5.4 --- Transfer Experiments on the Image Sequence of a Human Face Model --- p.5.33 / References --- p.5.44 / Chapter Chapter 6 --- Conclusions and Future Researches / Chapter 6.1 --- Contributions and Conclusions --- p.6.1 / Chapter 6.2 --- Future Researches --- p.6.1 / Bibliography --- p.B.1

by Chan Kwong Wing Raymond. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 115-123). / Acknowledgments --- p.i / Table of Contents --- p.ii-v / List of Tables --- p.vi / List of Figures --- p.vii / Abstract --- p.1 / Chapter Chapter 1 : --- Introduction / Chapter 1.1 --- An Introduction to Video Compression --- p.3 / Chapter 1.2 --- Overview of Problems --- p.4 / Chapter 1.2.1 --- Analog Video and Digital Problems --- p.4 / Chapter 1.2.2 --- Low Bit Rate Application Problems --- p.4 / Chapter 1.2.3 --- Real Time Video Compression Problems --- p.5 / Chapter 1.2.4 --- Source Coding and Channel Coding Problems --- p.6 / Chapter 1.2.5 --- Bit-rate and Quality Problems --- p.7 / Chapter 1.3 --- Organization of the Thesis --- p.7 / Chapter Chapter 2 : --- Background and Related Work / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- Analog Video --- p.9 / Chapter 2.1.2 --- Digital Video --- p.10 / Chapter 2.1.3 --- Color Theory --- p.10 / Chapter 2.2 --- Video Coding --- p.12 / Chapter 2.2.1 --- Predictive Coding --- p.12 / Chapter 2.2.2 --- Vector Quantization --- p.12 / Chapter 2.2.3 --- Subband Coding --- p.13 / Chapter 2.2.4 --- Transform Coding --- p.14 / Chapter 2.2.5 --- Hybrid Coding --- p.14 / Chapter 2.3 --- Transform Coding --- p.15 / Chapter 2.3.1 --- Discrete Cosine Transform --- p.16 / Chapter 2.3.1.1 --- 1-D Fast Algorithms --- p.16 / Chapter 2.3.1.2 --- 2-D Fast Algorithms --- p.17 / Chapter 2.3.1.3 --- Multidimensional DCT Algorithms --- p.17 / Chapter 2.3.2 --- Quantization --- p.18 / Chapter 2.3.3 --- Entropy Coding --- p.18 / Chapter 2.3.3.1 --- Huffman Coding --- p.19 / Chapter 2.3.3.2 --- Arithmetic Coding --- p.19 / Chapter Chapter 3 : --- Existing Compression Scheme / Chapter 3.1 --- Introduction --- p.20 / Chapter 3.2 --- Motion JPEG --- p.20 / Chapter 3.3 --- MPEG --- p.20 / Chapter 3.4 --- H.261 --- p.22 / Chapter 3.5 --- Other Techniques --- p.23 / Chapter 3.5.1 --- Fractals --- p.23 / Chapter 3.5.2 --- Wavelets --- p.23 / Chapter 3.6 --- Proposed Solution --- p.24 / Chapter 3.7 --- Summary --- p.25 / Chapter Chapter 4 : --- Fast 3D-DCT Algorithms / Chapter 4.1 --- Introduction --- p.27 / Chapter 4.1.1 --- Motivation --- p.27 / Chapter 4.1.2 --- Potentials of 3D DCT --- p.28 / Chapter 4.2 --- Three Dimensional Discrete Cosine Transform (3D-DCT) --- p.29 / Chapter 4.2.1 --- Inverse 3D-DCT --- p.29 / Chapter 4.2.2 --- Forward 3D-DCT --- p.30 / Chapter 4.3 --- 3-D FCT (3-D Fast Cosine Transform Algorithm --- p.30 / Chapter 4.3.1 --- Partitioning and Rearrangement of Data Cube --- p.30 / Chapter 4.3.1.1 --- Spatio-temporal Data Cube --- p.30 / Chapter 4.3.1.2 --- Spatio-temporal Transform Domain Cube --- p.31 / Chapter 4.3.1.3 --- Coefficient Matrices --- p.31 / Chapter 4.3.2 --- 3-D Inverse Fast Cosine Transform (3-D IFCT) --- p.32 / Chapter 4.3.2.1 --- Matrix Representations --- p.32 / Chapter 4.3.2.2 --- Simplification of the calculation steps --- p.33 / Chapter 4.3.3 --- 3-D Forward Fast Cosine Transform (3-D FCT) --- p.35 / Chapter 4.3.3.1 --- Decomposition --- p.35 / Chapter 4.3.3.2 --- Reconstruction --- p.36 / Chapter 4.4 --- The Fast Algorithm --- p.36 / Chapter 4.5 --- Example using 4x4x4 IFCT --- p.38 / Chapter 4.6 --- Complexity Comparison --- p.43 / Chapter 4.6.1 --- Complexity of Multiplications --- p.43 / Chapter 4.6.2 --- Complexity of Additions --- p.43 / Chapter 4.7 --- Implementation Issues --- p.44 / Chapter 4.8 --- Summary --- p.46 / Chapter Chapter 5 : --- Quantization / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- Dynamic Ranges of 3D-DCT Coefficients --- p.49 / Chapter 5.3 --- Distribution of 3D-DCT AC Coefficients --- p.54 / Chapter 5.4 --- Quantization Volume --- p.55 / Chapter 5.4.1 --- Shifted Complement Hyperboloid --- p.55 / Chapter 5.4.2 --- Quantization Volume --- p.58 / Chapter 5.5 --- Scan Order for Quantized 3D-DCT Coefficients --- p.59 / Chapter 5.6 --- Finding Parameter Values --- p.60 / Chapter 5.7 --- Experimental Results from Using the Proposed Quantization Values --- p.65 / Chapter 5.8 --- Summary --- p.66 / Chapter Chapter 6 : --- Entropy Coding / Chapter 6.1 --- Introduction --- p.69 / Chapter 6.1.1 --- Huffman Coding --- p.69 / Chapter 6.1.2 --- Arithmetic Coding --- p.71 / Chapter 6.2 --- Zero Run-Length Encoding --- p.73 / Chapter 6.2.1 --- Variable Length Coding in JPEG --- p.74 / Chapter 6.2.1.1 --- Coding of the DC Coefficients --- p.74 / Chapter 6.2.1.2 --- Coding of the DC Coefficients --- p.75 / Chapter 6.2.2 --- Run-Level Encoding of the Quantized 3D-DCT Coefficients --- p.76 / Chapter 6.3 --- Frequency Analysis of the Run-Length Patterns --- p.76 / Chapter 6.3.1 --- The Frequency Distributions of the DC Coefficients --- p.77 / Chapter 6.3.2 --- The Frequency Distributions of the DC Coefficients --- p.77 / Chapter 6.4 --- Huffman Table Design --- p.84 / Chapter 6.4.1 --- DC Huffman Table --- p.84 / Chapter 6.4.2 --- AC Huffman Table --- p.85 / Chapter 6.5 --- Implementation Issue --- p.85 / Chapter 6.5.1 --- Get Category --- p.85 / Chapter 6.5.2 --- Huffman Encode --- p.86 / Chapter 6.5.3 --- Huffman Decode --- p.86 / Chapter 6.5.4 --- PutBits --- p.88 / Chapter 6.5.5 --- GetBits --- p.90 / Chapter Chapter 7 : --- "Contributions, Concluding Remarks and Future Work" / Chapter 7.1 --- Contributions --- p.92 / Chapter 7.2 --- Concluding Remarks --- p.93 / Chapter 7.2.1 --- The Advantages of 3D DCT codec --- p.94 / Chapter 7.2.2 --- Experimental Results --- p.95 / Chapter 7.1 --- Future Work --- p.95 / Chapter 7.2.1 --- Integer Discrete Cosine Transform Algorithms --- p.95 / Chapter 7.2.2 --- Adaptive Quantization Volume --- p.96 / Chapter 7.2.3 --- Adaptive Huffman Tables --- p.96 / Appendices: / Appendix A : The detailed steps in the simplification of Equation 4.29 --- p.98 / Appendix B : The program Listing of the Fast DCT Algorithms --- p.101 / Appendix C : Tables to Illustrate the Reording of the Quantized Coefficients --- p.110 / Appendix D : Sample Values of the Quantization Volume --- p.111 / Appendix E : A 16-bit VLC table for AC Run-Level Pairs --- p.113 / References --- p.115

by Ho, Hau Lai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 97-103). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of Attractor Image Coding --- p.2 / Chapter 1.2 --- Scope of Thesis --- p.3 / Chapter 2 --- Fundamentals of Attractor Coding --- p.6 / Chapter 2.1 --- Notations --- p.6 / Chapter 2.2 --- Mathematical Preliminaries --- p.7 / Chapter 2.3 --- Partitioned Iterated Function Systems --- p.10 / Chapter 2.3.1 --- Mathematical Formulation of the PIFS --- p.12 / Chapter 2.4 --- Attractor Coding using the PIFS --- p.16 / Chapter 2.4.1 --- Quadtree Partitioning --- p.18 / Chapter 2.4.2 --- Inclusion of an Orthogonalization Operator --- p.19 / Chapter 2.5 --- Coding Examples --- p.21 / Chapter 2.5.1 --- Evaluation Criterion --- p.22 / Chapter 2.5.2 --- Experimental Settings --- p.22 / Chapter 2.5.3 --- Results and Discussions --- p.23 / Chapter 2.6 --- Summary --- p.25 / Chapter 3 --- Attractor Coding with Adjacent Block Parameter Estimations --- p.27 / Chapter 3.1 --- δ-Minimum Edge Difference --- p.29 / Chapter 3.1.1 --- Definition --- p.29 / Chapter 3.1.2 --- Theoretical Analysis --- p.31 / Chapter 3.2 --- Adjacent Block Parameter Estimation Scheme --- p.33 / Chapter 3.2.1 --- Joint Optimization --- p.34 / Chapter 3.2.2 --- Predictive Coding --- p.36 / Chapter 3.3 --- Algorithmic Descriptions of the Proposed Scheme --- p.39 / Chapter 3.4 --- Experimental Results --- p.40 / Chapter 3.5 --- Summary --- p.50 / Chapter 4 --- Attractor Coding using Lapped Partitioned Iterated Function Sys- tems --- p.51 / Chapter 4.1 --- Lapped Partitioned Iterated Function Systems --- p.53 / Chapter 4.1.1 --- Weighting Operator --- p.54 / Chapter 4.1.2 --- Mathematical Formulation of the LPIFS --- p.57 / Chapter 4.2 --- Attractor Coding using the LPIFS --- p.62 / Chapter 4.2.1 --- Choice of Weighting Operator --- p.64 / Chapter 4.2.2 --- Range Block Preprocessing --- p.69 / Chapter 4.2.3 --- Decoder Convergence Analysis --- p.73 / Chapter 4.3 --- Local Domain Block Searching --- p.74 / Chapter 4.3.1 --- Theoretical Foundation --- p.75 / Chapter 4.3.2 --- Local Block Searching Algorithm --- p.77 / Chapter 4.4 --- Experimental Results --- p.79 / Chapter 4.5 --- Summary --- p.90 / Chapter 5 --- Conclusion --- p.91 / Chapter 5.1 --- Original Contributions --- p.91 / Chapter 5.2 --- Subjects for Future Research --- p.92 / Chapter A --- Fundamental Definitions --- p.94 / Chapter B --- Appendix B --- p.96 / Bibliography --- p.97

Shan, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 124-132). / Abstracts in English and Chinese. / Chapter 1 --- Overview --- p.1 / Chapter 1.1 --- Image Blur Overview --- p.1 / Chapter 1.2 --- Blur Identification in a Transparency's Perspective --- p.3 / Chapter 1.3 --- From Transparencies to Natural Image Priors --- p.7 / Chapter 1.4 --- Discussion of the Linear Motion Model --- p.9 / Chapter 1.5 --- Binary Texture Restoration and High-Order MRF Optimization --- p.9 / Chapter 2 --- A Review on Previous Work --- p.13 / Chapter 2.1 --- Spatially-Invariant Blur Recovery --- p.13 / Chapter 2.2 --- Spatially-Variant Blur Recovery --- p.16 / Chapter 2.3 --- Markov Random Field Inference --- p.18 / Chapter 3 --- Motion Blur in a Transparency's Perspective --- p.20 / Chapter 3.1 --- Analysis of Object Motion Blur --- p.20 / Chapter 3.1.1 --- 1D Object Motion Blur --- p.20 / Chapter 3.1.2 --- 2D Object Motion Blur --- p.23 / Chapter 3.2 --- Modeling 2D Object Motion Blur --- p.26 / Chapter 3.3 --- Optimization Procedure --- p.27 / Chapter 3.3.1 --- Blur Kernel Estimation --- p.29 / Chapter 3.3.2 --- Latent Binary Matte Estimation --- p.30 / Chapter 3.4 --- Generalized Transparency in Motion Blur --- p.33 / Chapter 3.4.1 --- Camera Motion Blur Estimation --- p.35 / Chapter 3.4.2 --- Implementation --- p.37 / Chapter 3.5 --- Analysis and Results --- p.38 / Chapter 3.5.1 --- Evaluation of the Kernel Initialization --- p.40 / Chapter 3.5.2 --- Evaluation of Binary Alpha Initialization --- p.40 / Chapter 3.5.3 --- Robustness to Noise --- p.41 / Chapter 3.5.4 --- Natural Image Deblurring Results --- p.41 / Chapter 3.6 --- Proofs --- p.50 / Chapter 4 --- Rotational Motion Deblurring --- p.55 / Chapter 4.1 --- Motion blur descriptor --- p.55 / Chapter 4.1.1 --- Descriptor analysis --- p.56 / Chapter 4.2 --- Optimization --- p.59 / Chapter 4.2.1 --- Parameter initialization --- p.59 / Chapter 4.2.2 --- Iterative optimization --- p.62 / Chapter 4.2.3 --- Recover the color image --- p.65 / Chapter 4.3 --- Result and analysis --- p.65 / Chapter 5 --- Image Deblurring using Natural Image Priors --- p.70 / Chapter 5.1 --- Problem Definition --- p.70 / Chapter 5.2 --- Analysis of Ringing Artifacts --- p.71 / Chapter 5.3 --- Our model --- p.74 / Chapter 5.3.1 --- Definition of the probability terms --- p.75 / Chapter 5.4 --- Optimization --- p.81 / Chapter 5.4.1 --- Optimizing L --- p.83 / Chapter 5.4.2 --- Optimizing f --- p.86 / Chapter 5.4.3 --- Optimization Details and Parameters --- p.87 / Chapter 5.5 --- Experimental Results --- p.90 / Chapter 6 --- High Order MRF and its Optimization --- p.94 / Chapter 6.1 --- The Approach --- p.95 / Chapter 6.1.1 --- Polynomial Standardization --- p.95 / Chapter 6.1.2 --- Polynomial Graph Construction --- p.97 / Chapter 6.1.3 --- Polynomial Graph Partition --- p.103 / Chapter 6.1.4 --- Multi-Label Expansion --- p.105 / Chapter 6.1.5 --- Analysis --- p.106 / Chapter 6.2 --- Experimental Results --- p.108 / Chapter 6.3 --- Summary --- p.112 / Chapter 6.4 --- Proofs --- p.112 / Chapter 7 --- Conclusion --- p.117 / Chapter 7.1 --- Solving Linear Motion Blur in a Transparency's Perspective --- p.117 / Chapter 7.2 --- Rotational Motion Deblurring --- p.119 / Chapter 7.3 --- Image Deblurring using Natural Image Priors --- p.119 / Chapter 7.4 --- Contribution --- p.121 / Chapter 7.5 --- Discussion and Open Questions --- p.121 / Bibliography --- p.124

Leung, Man Kin. / Thesis submitted in: October 2008. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 121-126). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Literature review --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Background knowledge --- p.9 / Chapter 2.2.1 --- Pinhole camera model --- p.10 / Chapter 2.2.2 --- Kalman filter --- p.11 / Chapter 2.2.3 --- Extended Kalman filter --- p.14 / Chapter 2.2.4 --- Unscented Kalman filter --- p.15 / Chapter 2.3 --- Batch method --- p.19 / Chapter 2.3.1 --- Multiple view geometry --- p.19 / Chapter 2.3.2 --- Factorization --- p.21 / Chapter 2.3.3 --- Bundle adjustment --- p.22 / Chapter 2.4 --- Sequential method --- p.23 / Chapter 2.5 --- SLAM using cameras --- p.24 / Chapter 2.6 --- Summary --- p.26 / Chapter 3 --- Pose tracking of a stereo camera system --- p.27 / Chapter 3.1 --- Overview --- p.27 / Chapter 3.1.1 --- Related work --- p.27 / Chapter 3.1.2 --- Contribution --- p.29 / Chapter 3.2 --- Problem definition --- p.29 / Chapter 3.3 --- Algorithm --- p.31 / Chapter 3.3.1 --- Initialization --- p.33 / Chapter 3.3.2 --- Feature tracking and stereo correspondence matching --- p.33 / Chapter 3.3.3 --- Pose tracking based on two trifocal tensors --- p.35 / Chapter 3.3.4 --- Pose tracking using extended Kalman filter (Our EKF-2 approach) --- p.37 / Chapter 3.3.5 --- Pose tracking using unscented Kalman filter (Our UKF-2 approach) --- p.41 / Chapter 3.3.6 --- Pose tracking using differential evolution (Our DE-2 approach) --- p.44 / Chapter 3.4 --- Experiment --- p.49 / Chapter 3.4.1 --- Synthetic experiments --- p.49 / Chapter 3.4.2 --- Real experiments --- p.55 / Chapter 3.5 --- Summary --- p.67 / Chapter 4 --- Advance to two pairs of stereo cameras --- p.68 / Chapter 4.1 --- Overview --- p.68 / Chapter 4.1.1 --- Related work --- p.68 / Chapter 4.1.2 --- Contribution --- p.69 / Chapter 4.2 --- Problem definition --- p.70 / Chapter 4.3 --- Algorithm --- p.72 / Chapter 4.3.1 --- Initialization --- p.72 / Chapter 4.3.2 --- Feature tracking and stereo correspondence matching --- p.74 / Chapter 4.3.3 --- Pose tracking based on four trifocal tensors --- p.76 / Chapter 4.3.4 --- Pose tracking using extended Kalman filter (Our EKF-4 approach) --- p.79 / Chapter 4.3.5 --- Pose tracking using unscented Kalman filter (Our UKF-4 approach) --- p.84 / Chapter 4.4 --- Experiment --- p.87 / Chapter 4.4.1 --- Synthetic experiments --- p.87 / Chapter 4.4.2 --- Real experiments --- p.100 / Chapter 4.5 --- Summary --- p.113 / Chapter 5 --- Conclusion --- p.115 / Chapter 5.1 --- Conclusion --- p.115 / Chapter 5.2 --- Scope of Applications --- p.116 / Chapter 5.3 --- Limitations --- p.117 / Chapter 5.4 --- Difficulties --- p.118 / Chapter 5.5 --- Future work --- p.118 / Bibliography --- p.121