abstract / Computer Science / Doctoral / Doctor of Philosophy

abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

The problems of noise removal, and simultaneous noise removal and deblurring of imagery are common to many areas of science. An approach which allows for the unified treatment of both problems involves modeling imagery as a sample of a random process. Various nonstationary image models are explored in this context. Attention is directed to identifying the model parameters from imagery which has been corrupted by noise and possibly blur, and the use of the model to form an optimal reconstruction of the image. Throughout the work, emphasis is placed on both theoretical development and practical considerations involved in achieving this reconstruction. The results indicate that the use of nonstationary image models offers considerable improvement over traditional techniques.

This paper proposes a metric to predict edge detection performance when applied to an image with noise. First, models of edges and edge detection linear operators are characterized by their spatial and Fourier domain properties. Second, additive uncorrelated noise on the operator is examined and a metric is developed using the image formation system modulation transfer function (MTF), expected noise power spectral density, and edge detector characterization as inputs. Thirdly, the problem of partially correlated noise is examined. A separate performance metric for simple thresholded operator outputs is proposed. Finally, several discrete edge detectors in noise are evaluated numerically. Both the metric based on signal to noise detector output, and based on thresholding probabilities were useful in predicting previously published performance results. This was true even for many nonlinear detectors based on the linear detectors evaluated here. The specification of a localization criteria was critical for comparisons between detectors.

by Ringo Wai-kit Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / ACKNOWLEDGEMENTS / ABSTRACT / LIST OF FIGURES --- p.i / LIST OF TABLES --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Introduction --- p.1-1 / Chapter 1.2 --- Transform Coding Theory --- p.1-2 / Chapter 1.2.1 --- Image Transform Coder and Decoder --- p.1-2 / Chapter 1.2.2 --- Transformation --- p.1-4 / Chapter 1.2.3 --- Bit Allocation --- p.1-5 / Chapter 1.2.4 --- Quantization --- p.1-7 / Chapter 1.2.5 --- Entropy Coding --- p.1-8 / Chapter 1.2.6 --- Error of Transform Coding --- p.1-9 / Chapter 1.3 --- Organization of The Thesis --- p.1-10 / Chapter CHAPTER 2 --- 2D INTEGER COSINE TRANSFORM CHIP SET --- p.2-1 / Chapter 2.1 --- Introduction --- p.2-1 / Chapter 2.2 --- The Integer Cosine Transform (ICT) --- p.2-2 / Chapter 2.3 --- LSI Implementation --- p.2-4 / Chapter 2.3.1 --- ICT Chip --- p.2-4 / Chapter 2.3.2 --- Data Sequencer --- p.2-7 / Chapter 2.4 --- Design Considerations --- p.2-8 / Chapter 2.4.1 --- ICT chip --- p.2-9 / Chapter 2.4.1.1 --- Specifications --- p.2-9 / Chapter 2.4.1.2 --- I/O Bit Length Consideration --- p.2-10 / Chapter 2.4.1.3 --- Selection of The Transform Matrix --- p.2-12 / Chapter 2.4.2 --- Data Sequencer --- p.2-16 / Chapter 2.4.2.1 --- Normal Operation --- p.2-16 / Chapter 2.4.2.2 --- Low-pass Filtering Operation --- p.2-16 / Chapter 2.4.2.3 --- Subsampling Operation --- p.2-17 / Chapter 2.5 --- Architecture --- p.2-18 / Chapter 2.5.1 --- ICT chip --- p.2-18 / Chapter 2.5.1.1 --- Input Stage --- p.2-18 / Chapter 2.5.1.2 --- Control Block --- p.2-19 / Chapter 2.5.1.3 --- Multiplier --- p.2-19 / Chapter 2.5.1.4 --- Accumulator --- p.2-20 / Chapter 2.5.1.5 --- Output Stage --- p.2-21 / Chapter 2.5.2 --- Data Sequencer --- p.2-21 / Chapter 2.5.2.1 --- Input Stage --- p.2-22 / Chapter 2.5.2.2 --- Control Logic --- p.2-22 / Chapter 2.5.2.3 --- Internal Storage --- p.2-23 / Chapter 2.5.2.4 --- Output Stage --- p.2-24 / Chapter 2.6 --- 2D Integer Cosine Transform System --- p.2-24 / Chapter 2.6.1 --- Hardware Architecture --- p.2-24 / Chapter 2.6.2 --- Timing --- p.2-26 / Chapter 2.7 --- Conclusion --- p.2-27 / Chapter CHAPTER 3 --- A PC/AT-BASED IMAGE ARCHIVING SYSTEM --- p.3-1 / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Design Consideration --- p.3-1 / Chapter 3.2.1 --- Specifications --- p.3-2 / Chapter 3.2.1.1 --- Operations Supported --- p.3-2 / Chapter 3.2.1.2 --- Image Formats --- p.3-3 / Chapter 3.2.1.3 --- Software --- p.3-6 / Chapter 3.2.2 --- Storage Format of the Coded Image --- p.3-6 / Chapter 3.3 --- Hardware Architecture --- p.3-8 / Chapter 3.3.1 --- Input Stage --- p.3-11 / Chapter 3.3.2 --- Inverse Transform Address Generator --- p.3-11 / Chapter 3.3.3 --- Input Memory --- p.3-13 / Chapter 3.3.3.1 --- Address Map --- p.3-14 / Chapter 3.3.3.2 --- Bit Map --- p.3-14 / Chapter 3.3.3.3 --- Class Map --- p.3-15 / Chapter 3.3.4 --- ICT Processor --- p.3-15 / Chapter 3.3.5 --- Output Memory --- p.3-16 / Chapter 3.3.6 --- Address Generator --- p.3-16 / Chapter 3.3.6.1 --- Address Generator 1 (AG1) --- p.3-17 / Chapter 3.3.6.2 --- Address Generator 2 (AG2) --- p.3-21 / Chapter 3.3.6.3 --- Address Generator 3 (AG3) --- p.3-22 / Chapter 3.3.7 --- Control Register --- p.3-22 / Chapter 3.3.8 --- Interface Consideration --- p.3-23 / Chapter 3.3.9 --- Frame Buffer --- p.3-23 / Chapter 3.4 --- Software Structure --- p.3-23 / Chapter 3.4.1 --- Main Menu --- p.3-24 / Chapter 3.4.2 --- Forward Transform --- p.3-25 / Chapter 3.4.3 --- Inverse Transform --- p.3-25 / Chapter 3.4.3.1 --- Normal --- p.3-26 / Chapter 3.4.3.2 --- Subsampling --- p.3-26 / Chapter 3.4.3.3 --- Filtering --- p.3-26 / Chapter 3.4.3.4 --- Album --- p.3-27 / Chapter 3.4.3.5 --- Display and System --- p.3-28 / Chapter 3.5 --- Conclusion --- p.3-29 / Chapter CHAPTER 4 --- SYSTEM PERFORMANCE EVALUATION --- p.4-1 / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.2 --- Result of Image Display --- p.4-1 / Chapter 4.3 --- Computation Time Requirement --- p.4-12 / Chapter 4.4 --- Comparison to Other Transform Chips and Image Transform Systems --- p.4-16 / Chapter 4.5 --- Conclusion --- p.4-20 / Chapter CHAPTER 5 --- CONCLUSION --- p.5-1 / Chapter 5.1 --- Further Development --- p.5-1 / Chapter 5.1.1 --- Employment of JPEG Scheme --- p.5-1 / Chapter 5.1.2 --- ICT Chip Set --- p.5-5 / Chapter 5.2 --- Summary of the Image Archiving System --- p.5-6 / Chapter CHAPTER 6 --- REFERENCES --- p.6-1 / Chapter CHAPTER 7 --- APPENDIX --- p.7-1

Xu, Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 62-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Organization --- p.4 / Chapter 2 --- Background and Motivation --- p.7 / Chapter 2.1 --- ID Mondrian Smoothing --- p.9 / Chapter 2.2 --- 2D Formulation --- p.13 / Chapter 3 --- Solver --- p.16 / Chapter 3.1 --- More Analysis --- p.20 / Chapter 4 --- Edge Extraction --- p.26 / Chapter 4.1 --- Related work --- p.26 / Chapter 4.2 --- Method and Results --- p.28 / Chapter 4.3 --- Summary --- p.32 / Chapter 5 --- Image Abstraction and Pencil Sketching --- p.35 / Chapter 5.1 --- Related Work --- p.35 / Chapter 5.2 --- Method and Results --- p.36 / Chapter 5.3 --- Summary --- p.40 / Chapter 6 --- Clip-Art Compression Artifact Removal --- p.41 / Chapter 6.1 --- Related work --- p.41 / Chapter 6.2 --- Method and Results --- p.43 / Chapter 6.3 --- Summary --- p.46 / Chapter 7 --- Layer-Based Contrast Manipulation --- p.49 / Chapter 7.1 --- Related Work --- p.49 / Chapter 7.2 --- Method and Results --- p.50 / Chapter 7.2.1 --- Edge Adjustment --- p.51 / Chapter 7.2.2 --- Detail Magnification --- p.54 / Chapter 7.2.3 --- Tone Mapping --- p.55 / Chapter 7.3 --- Summary --- p.56 / Chapter 8 --- Conclusion and Discussion --- p.59 / Bibliography --- p.61

Image segmentation is about how an image could be divided into pieces or segments so that each segment corresponds to a surface or an object which demonstrates high degree of uniformity in its visual appearance. Automatic image segmentation method must in a way make use of the uniformity property of the segments. The uniformity property of a segment however manifests in a number of ways, forming different channels of features. Since the segment's appearance is uniform within itself, the intensities or textures in the image of the segment must look rather similar, and they together are what the literature calls the region-level features. Since a segment's appearance uniformity must be different from those of the immediate neighboring segments, or else they should not be referred to as different segments, the boundary of a segment therefore should exhibit high degree of intensity contrast in the image data, and such contrast leads to edgel features (which is often referred to as boundary features) in the image data. Other channels of features include discrete labels assigned to image pixels according to their intensity levels, and certain prior knowledge of the object shape in the image. / In the first piece of work, an approach of gray level image segmentation is investigated, which uses boundary feature and region feature complementarity. In this approach, the line segments, which are derived by grouping edge elements, are used to construct a saliency map to indicate the location likelihood of the real boundaries. The closed boundaries extracted from the saliency map are then refined by a region based active contour method. The scheme allows the challenging issues of boundary closure and segmentation accuracy to be both addressed. / In the second piece of work, an approach of foreground-background segmentation is explored, which integrates the boundary features and certain labels assigned to the image pixels according to their intensity levels. The labels are in accordance with certain coarse clustering over the intensity histogram of the image. In this approach, an inhomogeneity measure is encoded in a variational formulation, thus the measure can be applied to the entire image domain and be made global. The approach has a uniform treatment to gray level, color and texture images. In addition, the approach allows explicit encouragement on the smoothness of the segmentation boundary by using the level set technique-based active contour method. / In the third piece of work, an approach of foreground-background segmentation is investigated, that makes use of both the boundary features and certain prior knowledge of object shape. This approach can also be categorized as an object detection method. In this approach, we adopt a new multiplicative formulation to combine the edgel information and the prior shape knowledge. The method reduces the number of system parameters and increases the algorithm's robustness. / Much of the previous work on image segmentation is based upon features of a particular channel, such as the edgel features, the region features, or others. The objective of this thesis is to explore how features of multiple channels can be put together integratively, or more precisely in an active contour deformation process under the level set formulation, for more accurate image segmentation. Three combinations of features are investigated. The first is about integrating the boundary features and region features. The second is about integrating the boundary features and the labels to pixels according to certain coarse intensity clustering. The third is about integrating the boundary features and certain prior knowledge of the object shape. / The proposed algorithms in this thesis have been tested on many real and synthetic images. The experimental results illustrate their efficacy and limitation. / Wang, Wei. / "September 2007." / Adviser: Ronald Chi-kit Chung. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4865. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 39-106). / 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, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

He Yong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 69-72). / Abstracts in English and Chinese. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.v / TABLE OF CONTENTS --- p.vi / LIST OF FIGURES --- p.viii / LIST OF TABLES --- p.x / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 2 --- PRELIMINARY --- p.6 / Chapter 3 --- IMAGE MOSAICING FOR SINGLY VISIBLE SURFACES --- p.9 / Chapter 3.1 --- Background --- p.9 / Chapter 3.2 --- Correspondence Inference Mechanism --- p.13 / Chapter 3.3 --- Seamless Lining up of Surface Boundary --- p.17 / Chapter 3.4 --- Experimental Result --- p.21 / Chapter 3.5 --- Summary of Image Mosaicing Work --- p.32 / Chapter 4 --- MOBILE ROBOT SELF-LOCALIZATION FROM MONOCULAR VISION --- p.33 / Chapter 4.1 --- Background --- p.33 / Chapter 4.2 --- Problem Definition --- p.37 / Chapter 4.3 --- Our Strategy of Localizing the Mobile Robot --- p.38 / Chapter 4.3.1 --- Establishing Correspondences --- p.40 / Chapter 4.3.2 --- Determining Position from Factorizing E-matrix --- p.49 / Chapter 4.3.3 --- Improvement on the Factorization Result --- p.55 / Chapter 4.4 --- Experimental Result --- p.56 / Chapter 4.5 --- Summary of Mobile Robot Self-localization Work --- p.62 / Chapter 5 --- CONCLUSION AND FUTURE WORK --- p.63 / APPENDIX --- p.67 / BIBLIOGRAPHY --- p.69

隨著數碼相機、攝影手機以及監控攝像機的快速普及，每天無數的視頻錄像被創造出來。運動估計是視頻處理中的一種基本問題，這個問題通常被稱為光流估計。現有光流估計算法無法處理發生較大尺度變化的視頻。但尺度變化在視頻和照片中非常普遍，所以尺度不變性的光流估計算法對於其他視頻處理操作諸如圖像去噪算法有很大幫助。所以我們提出新的方法來解決這個問題，以建立兩幀圖像不同尺度像素之間的稠密匹配。我們提出一個新的框架，引入像素級精度的尺度參數，然後提出一種有效的數值計算機制，迭代地優化離散尺度參數和連續光流參數。這個機制顯著地拓展了光流估計在包含各種類型運動的自然場景的實用性。 / 各種攝像設備獲得的視頻都不同程度地遭到噪聲的破壞。雖然已經提出許多視頻去噪算法，但在實際應用中仍然存在許多問題。所以，我們設計一種複雜度很低而且有效的實時視頻去噪算法。我們在視頻去噪的過程中引入高品質的光流估計來校準圖像序列。我們還設計了一種加權平均算法來從之前校準的原始視頻幀中恢復出沒有噪聲的圖像。實驗結果表明相比于其他算法，我們的算法能恢復出更多的細節。更重要的是，我們的算法保證視頻的時域連貫性，對視頻質量來說非常重要。 / 最後，我們還研究了光照不足的環境下拍攝的視頻和圖像中常見的有色噪聲現象。這種噪聲沒有辦法被現有算法有效地去除，因為它們通常假設噪聲是一個高斯或泊松分佈。根據我們對亮度噪聲和色度噪聲的觀察和分析，我們提出了一種新的去噪方法。我們採用了多分辨率雙重雙邊濾波的方法，借用現有算法去噪的亮度層來引導色度層的去噪。實驗表明，視覺和數據評價都表明了我們算法的有效性。 / With the popularity of digital cameras, mobile phone cameras and surveillance systems, numerous video clips are created everyday. Motion estimation is one of the fundamental tasks in video processing. Current optical flow estimation algorithms cannot deal with frames that are with large scale variation. Because scale variation commonly arises in images/videos, a scale invariant optical flow algorithm is important and fundamental for other video operations such as video denoising. In light of this, we propose a new method, aiming to establish dense correspondence between two frames containing pixels in different scales. We contribute a new framework taking pixel-wise scale into consideration in optical flow estimation and propose an effective numerical scheme, which iteratively optimizes discrete scale variables and continuous flow ones. This scheme notably expands the practicality of optical flow in natural scenes containing different types of object movements. / Further, Videos captured by all kinds of sensors are generally contaminated by noise. Although lots of algorithms are published, there are still many problems when applying them to real cases. We design a low-complexity but effective real-time video denoising framework by integrating robust optical flow estimation into the denoising process to register locally frame sequences and designing a weighted averaging algorithm to restore a latent clean frame from a sequence of well registered frames. Experiments show that our algorithm recovers more details than other state-of-the-art video denoising algorithms. More importantly our method preserves temporal coherence, which is vital for videos. / Lastly, we study the chrominance noise which is commonly observed in both videos and images taken under insuficient light conditions. This kind of noise cannot be effectively reduced by state-of-the-art denoising methods under the assumption of a Gaussian or Poisson distributions. Based on the observation of the different characteristics of luminance and chrominance noise, we propose a new denoising strategy that employs multi-resolution dual bilateral filtering on chrominance layers un¬der the guidance of well-estimated luminance layer. Both visual and quantitative evaluation demonstrates the effectiveness of our algorithm. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Dai, Zhenlong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 81-90). / 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 --- Our Contributions --- p.6 / Chapter 1.3 --- Thesis Outline --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Optical Flow Estimation --- p.10 / Chapter 2.2 --- Single Image Denoising --- p.15 / Chapter 2.3 --- Multi-image and Video Denoising --- p.17 / Chapter 3 --- Scale Invariant Optical Flow --- p.20 / Chapter 3.1 --- Related Work --- p.23 / Chapter 3.2 --- Optical Flow Model with Scale Variables --- p.25 / Chapter 3.3 --- Optimization --- p.31 / Chapter 3.3.1 --- Computing E[zi] --- p.32 / Chapter 3.3.2 --- Minimizing Optical Flow Energy --- p.32 / Chapter 3.3.3 --- Overall Computation Framework --- p.34 / Chapter 3.4 --- Experiments --- p.37 / Chapter 3.4.1 --- Evaluation of Our Model to Handle Scales . --- p.37 / Chapter 3.4.2 --- Comparison with Other Optical Flow Methods . --- p.38 / Chapter 3.4.3 --- Comparison with Sparse Feature Matching . --- p.43 / Chapter 3.4.4 --- Evaluation on the Middlebury Dataset --- p.44 / Chapter 3.5 --- Summary --- p.46 / Chapter 4 --- Optical Flow Based Video Denoising --- p.47 / Chapter 4.1 --- Related Work --- p.48 / Chapter 4.2 --- Optical Flow based Video Denoising Framework --- p.48 / Chapter 4.2.1 --- Registration --- p.48 / Chapter 4.2.2 --- Accumulation --- p.52 / Chapter 4.2.3 --- Algorithm Implementation --- p.53 / Chapter 4.3 --- Experimental Results --- p.54 / Chapter 4.3.1 --- Comparisons with other algorithms --- p.54 / Chapter 4.3.2 --- Applications --- p.55 / Chapter 4.4 --- Limitation and Future Work --- p.55 / Chapter 4.5 --- Summary --- p.59 / Chapter 5 --- Chrominance Noise Reduction --- p.62 / Chapter 5.1 --- Related work --- p.65 / Chapter 5.2 --- Luminance and Chrominance Noise Characteristics --- p.68 / Chapter 5.3 --- Luminance and Chrominance Relationship --- p.69 / Chapter 5.4 --- Algorithm --- p.71 / Chapter 5.4.1 --- Dual Bilateral Filter --- p.71 / Chapter 5.4.2 --- Multi-resolution Framework --- p.72 / Chapter 5.5 --- Experiments --- p.72 / Chapter 5.5.1 --- Quantitative Evaluation --- p.73 / Chapter 5.5.2 --- Visual Comparison for Natural Noisy Images --- p.74 / Chapter 5.5.3 --- Applications --- p.75 / Chapter 5.6 --- Summary --- p.75 / Chapter 6 --- Conclusion --- p.79 / Bibliography --- p.82

Abstract not available