Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaves 56-57. / by Andrew Lippman. / M.S.

Sun, Deqing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 86-91). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Contributions --- p.iv / Acknowledgement --- p.vi / Abbreviations --- p.xviii / Notations --- p.xxi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Image compression and postprocessing --- p.1 / Chapter 1.2 --- A brief review of postprocessing --- p.3 / Chapter 1.3 --- Objective and methodology of the research --- p.7 / Chapter 1.4 --- Thesis organization --- p.8 / Chapter 1.5 --- A note on publication --- p.9 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Image models --- p.11 / Chapter 2.1.1 --- Minimum edge difference (MED) criterion for block boundaries --- p.12 / Chapter 2.1.2 --- van Beek's edge model for an edge --- p.15 / Chapter 2.1.3 --- Fields of experts (FoE) for an image --- p.16 / Chapter 2.2 --- Degradation models --- p.20 / Chapter 2.2.1 --- Quantization constraint set (QCS) and uniform noise --- p.21 / Chapter 2.2.2 --- Narrow quantization constraint set (NQCS) --- p.22 / Chapter 2.2.3 --- Gaussian noise --- p.22 / Chapter 2.2.4 --- Edge width enlargement after quantization --- p.25 / Chapter 2.3 --- Use of these models for postprocessing --- p.27 / Chapter 2.3.1 --- MED and edge models --- p.27 / Chapter 2.3.2 --- The FoE prior model --- p.27 / Chapter 3 --- Postprocessing using MED and edge models --- p.28 / Chapter 3.1 --- Blocking artifacts suppression by coefficient restoration --- p.29 / Chapter 3.1.1 --- AC coefficient restoration by MED --- p.29 / Chapter 3.1.2 --- General derivation --- p.31 / Chapter 3.2 --- Detailed algorithm --- p.34 / Chapter 3.2.1 --- Edge identification --- p.36 / Chapter 3.2.2 --- Region classification --- p.36 / Chapter 3.2.3 --- Edge reconstruction --- p.37 / Chapter 3.2.4 --- Image reconstruction --- p.37 / Chapter 3.3 --- Experimental results --- p.38 / Chapter 3.3.1 --- Results of the proposed method --- p.38 / Chapter 3.3.2 --- Comparison with one wavelet-based method --- p.39 / Chapter 3.4 --- On the global minimum of the edge difference . . --- p.41 / Chapter 3.4.1 --- The constrained minimization problem . . --- p.41 / Chapter 3.4.2 --- Experimental examination --- p.42 / Chapter 3.4.3 --- Discussions --- p.43 / Chapter 3.5 --- Conclusions --- p.44 / Chapter 4 --- Postprocessing by the MAP criterion using FoE --- p.49 / Chapter 4.1 --- The proposed method --- p.49 / Chapter 4.1.1 --- The MAP criterion --- p.49 / Chapter 4.1.2 --- The optimization problem --- p.51 / Chapter 4.2 --- Experimental results --- p.52 / Chapter 4.2.1 --- Setting algorithm parameters --- p.53 / Chapter 4.2.2 --- Results --- p.56 / Chapter 4.3 --- Investigation on the quantization noise model . . --- p.58 / Chapter 4.4 --- Conclusions --- p.61 / Chapter 5 --- Conclusion --- p.71 / Chapter 5.1 --- Contributions --- p.71 / Chapter 5.1.1 --- Extension of the DCCR algorithm --- p.71 / Chapter 5.1.2 --- Examination of the MED criterion --- p.72 / Chapter 5.1.3 --- Use of the FoE prior in postprocessing . . --- p.72 / Chapter 5.1.4 --- Investigation on the quantization noise model --- p.73 / Chapter 5.2 --- Future work --- p.73 / Chapter 5.2.1 --- Degradation model --- p.73 / Chapter 5.2.2 --- Efficient implementation of the MAP method --- p.74 / Chapter 5.2.3 --- Postprocessing of compressed video --- p.75 / Chapter A --- Detailed derivation of coefficient restoration --- p.76 / Chapter B --- Implementation details of the FoE prior --- p.81 / Chapter B.1 --- The FoE prior model --- p.81 / Chapter B.2 --- Energy function and its gradient --- p.83 / Chapter B.3 --- Conjugate gradient descent method --- p.84 / Bibliography --- p.86

Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general  waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.

Image processing refers to the various operations performed on pictures that are digitally stored as an aggregate of pixels. One can enhance or degrade the quality of an image, artistically transform the image, or even find or recognize objects within the image. This paper is concerned with image processing, but in a very mathematical perspective, involving representation theory. The approach traces back to Cooley and Tukey’s seminal paper on the Fast Fourier Transform (FFT) algorithm (1965). Recently, there has been a resurgence in investigating algebraic generalizations of this original algorithm with respect to different symmetry groups. My approach in the following chapters is as follows. First, I will give necessary tools from representation theory to explain how to generalize the Discrete Fourier Transform (DFT). Second, I will introduce wreath products and their application to images. Third, I will show some results from applying some elementary filters and compression methods to spectrums of images. Fourth, I will attempt to generalize my method to noncyclic wreath product transforms and apply it to images and three-dimensional geometries.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M.S.)--University of Delaware, 2007. / Principal faculty advisor: Gonzalo Arce, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph.D)--Stanford University, 1999. / Title from pdf t.p. (viewed May 9, 2002). "May 1999." "Adminitrivia V1/Prg/19990528"--Metadata.

Thesis (Ph. D.)--University of Delaware, 2007. / Principal faculty advisor: Gonzalo Arce, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (M.E.E.)--University of Delaware, 2006. / Principal faculty advisor: Kenneth E. Barner, Dept. of Electrical and Computer Engineering. Includes bibliographical references.