A new approach, based on maximum likelihood, is developed for binary object image restoration. This considers the image formation process as a stochastic process, with noise as a random variable. The likelihood function is constructed for the cases of Gaussian and Poisson noise. An uphill climb method is used to find the object, defined by its "grain" positions, through maximizing the likelihood function for grain positions. In addition, some a priori information regarding object size and contour of shapes is used. This is summarized as a "neighbouring point" rule. Some examples of computer generated images with different signal-to-noise ratios are used to show the ability of the algorithm. These cases include both Gaussian and Poisson noise. For noiseless and low noise Gaussian cases, a modified uphill climb method is used. The results show that binary objects are fairly well restored for all noise cases considered.

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

This paper first proposes a new quality metric for edge evaluation, based on six physical characteristics. These physical characteristics that affect human edge quality evaluation are continuity, smoothness, thinness, localization, detection and noisiness. The final edge quality score is a weighted linear combination of the quantified measures of these six edge quality attributes. The other feature of this edge evaluation metric is its adjustability. Through some training procedures, it can be adjusted to suit different user and application needs. In the latter part of this paper, an edge detector performance predictor is proposed. By a few initial measurements of image parameters, the performance of certain edge detectors can be predicted. Finally, the performance of several popular edge detectors is compared, under different variations of SNR, blurting and power spectrum.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Fong Chi Keung = 輪廓構圖法及其應用 / 馮志強. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Fong Chi Keung = Lun kuo gou tu fa ji qi ying yong / Feng Zhiqiang. / Acknowledgement --- p.i / Abstract --- p.ii / Contents --- p.iv / List of Figures --- p.vi / List of Tables --- p.xi / Chapter Chapter 1. --- Introduction --- p.1-1 / Chapter 1.1. --- A Brief Review on Image Representation --- p.1-1 / Chapter 1.2. --- Objective of the Research Work --- p.1-3 / Chapter 1.3. --- Organization of the Thesis --- p.1-4 / Chapter 1.3.1. --- The edge-model based representations --- p.1-4 / Chapter 1.3.2. --- The applications of edge-model based representation --- p.1-5 / Chapter Chapter 2. --- Review on the Edge Models --- p.2-1 / Chapter 2.1. --- Introduction --- p.2-1 / Chapter 2.2. --- Review on Existing Edge Models --- p.2-1 / Chapter 2.2.1. --- Unit-Step Model --- p.2-2 / Chapter 2.2.2. --- Ramp Model --- p.2-3 / Chapter 2.2.3. --- Hyperbolic Tangent Model --- p.2-4 / Chapter 2.2.4. --- van Beek's Edge Model --- p.2-5 / Chapter 2.3. --- Methodology --- p.2-6 / Chapter 2.3.1. --- Model Parameter Estimation in van Beek's model --- p.2-6 / Chapter 2.3.2. --- Model Parameter Estimation in other models --- p.2-9 / Chapter 2.3.3. --- Image Reconstruction --- p.2-10 / Chapter 2.3.4. --- Intensity Surface Reconstruction --- p.2-11 / Chapter 2.4. --- Summary --- p.2-18 / Chapter Chapter 3. --- Improved Edge-Model-Based representation --- p.3-1 / Chapter 3.1 --- Reconstruction Artifacts --- p.3-1 / Chapter 3.2 --- The improved edge model --- p.3-2 / Chapter 3.2.1. --- Minimum Reconstruction Range (MRR) --- p.3-2 / Chapter 3.2.2. --- Sub-pixel Estimation (SPE) --- p.3-4 / Chapter 3.3. --- Experimental Results --- p.3-11 / Chapter 3.3.1. --- Comparison between van Beek's Method and LSF in Parameters Estimation --- p.3-11 / Chapter 3.3.2. --- Comparison among Intensity Surface Reconstruction Methods --- p.3-13 / Chapter 3.3.3. --- Comparison among Edge Models --- p.3-18 / Chapter 3.4. --- Conclusions --- p.3-22 / Chapter Chapter 4. --- Edge-Model-Based Post-processing for SPIHT coded Images --- p.4-1 / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.2. --- Brief review on the Post-processing --- p.4-2 / Chapter 4.3. --- Experimental Results --- p.4-5 / Chapter 4.4. --- Conclusions --- p.4-6 / Chapter Chapter 5. --- Edge-Model-Based Interpolation --- p.5-1 / Chapter 5.1 --- Introduction --- p.5-1 / Chapter 5.2 --- Objectives --- p.5-6 / Chapter 5.3 --- Algorithm --- p.5-6 / Chapter 5.3.1. --- Edge Location Estimation --- p.5-7 / Chapter 5.3.2. --- Edge Width Correction --- p.5-10 / Chapter 5.3.3. --- Confident Function --- p.5-16 / Chapter 5.4 --- Experimental Results --- p.5-21 / Chapter 5.5 --- Conclusions --- p.5-32 / Chapter Chapter 6. --- Edge-model-based Image Segmentation --- p.6-1 / Chapter 6.1. --- Introduction --- p.6-1 / Chapter 6.2. --- A brief review on segmentation --- p.6-1 / Chapter 6.3. --- Objectives --- p.6-2 / Chapter 6.4. --- Theory --- p.6-3 / Chapter 6.5. --- Algorithm --- p.6-6 / Chapter 6.5.1. --- Pre-segmentation by edge-model --- p.6-6 / Chapter 6.5.2. --- Grouping by Gomory-Hu Tree --- p.6-8 / Chapter 6.6. --- Experimental Results --- p.6-10 / Chapter 6.7. --- Conclusions --- p.6-15 / Chapter Chapter 7. --- Conclusions and further developments --- p.7-1 / Chapter 7.1 --- Contributions and Conclusions --- p.7-1 / Chapter 7.1.1 --- Edge-Model-Based Post-Processing for SPIHT coded images --- p.7-1 / Chapter 7.1.2 --- Edge-Model-Based Interpolation --- p.7-2 / Chapter 7.1.3 --- Edge-Model-Based Segmentation --- p.7-2 / Chapter 7.2 --- Future Development --- p.7-3 / Appendix I. Test Images used in this Research --- p.I / Bibliography --- p.III

The problem of the restoration of quadratically distorted images is considered in this investigation, based upon the fact that images formed by partially coherent illuminations are related quadratically to the amplitude of the object. Two of the most important problems in image restoration are: 1) determining the degradation characteristics of the degraded image and 2) developing restoration algorithms. Among the two classes of inverse problems, one for system identification and the second for image restoration, only the means to solve the latter are presented in this study. Since the present problem is represented by the second-order term of a Volterra series expansion, multidimensional Volterra filter theory is presented with emphasis on the properties of two-dimensional quadratic filter. The mathematics of inverse problems is presented for the purpose of image restoration, and the novel algorithms which are simple and easy to implement and robust to the ill-conditioned system in comparison to the existing algorithms are proposed. Since quadratically distorted imaging systems preclude a closed-form solution, approximate solutions are obtained through application of the proposed iterative and noniterative schemes. Images restored approximately by the proposed algorithms can be improved substantially by the use of a Newton-Raphson iteration scheme. Two typical regularization methods are presented and the truncated singular-value decomposition method is applied for the noisy image restoration. Regularized iterative restoration schemes for the noisy image restoration are also considered. Simulation examples for different issues are presented. / Graduation date: 1991

Thesis(M.S.)--Case Western Reserve University, 2009 / Title from PDF (viewed on 2010-01-28) Department of Electrical Engineering and Computer Science -- Electrical Engineering Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.

Includes bibliographical references (p. 81-90).