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1 
Image reconstruction with multisensors /Sze, Nangkeung. January 2001 (has links)
Thesis (M. Phil.)University of Hong Kong, 2002. / Includes bibliographical references (leaves 5660).

2 
Analysis and improvement of total variation regularization /Lo, Wing Cheong. January 2006 (has links)
Thesis (M.Phil.)Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 5758). Also available in electronic version.

3 
Investigations on models and algorithms in variational approaches for image restorationFang, Yingying 17 August 2020 (has links)
Variational methods, which have proven to be very useful to solve the illposed inverse problems, have been generating a lot of research interest in the image restoration problem. It transforms the restoration problem into the optimization of a welldesigned variational model. While the designed model is convex, the recovered image is the global solution found by an appropriate numerical algorithm and the quality of the restored image depends on the accuracy of the designed model. Thus, a lot of eﬀorts have been put to propose a more precise model that can produce a result with more pleasing visual quality. Besides, due to the high dimension and the nonsmoothness of the imaging model, an eﬃcient algorithm to ﬁnd the exact solution of the variational model, is also of the research interest, since it inﬂuences the eﬃciency of the restoration techniques in the practical applications. In this thesis, we are interested in the designing of both the variational models for image restoration problems and the numerical algorithms to solve these models. The ﬁrst objective of this thesis is to make improvements on two models for image denoising. For the multiplicative noise removal, we designed a regularizer based on the statistical property of the speckle noise, which can transform the traditional model (named by AA) into a convex one. Therefore, a global solution can be found independent of the initialization of the numerical algorithm. Moreover, the regularization term added on the AA model can help produce a sharper result. The second model is improved on the traditional ROF model by adding an edge regularization which incorporates an edge prior obtained from the observed image. Extensive experiments show that designed edge regularization has superiority to increase the texture of the recovered result and remove the staircase artifacts in the meanwhile. It is also presented that the edge regularization designed can be easily adapted into other restoration task, such as image deblurring. The second objective of this thesis is to study the numerical algorithms for a general nonsmooth imaging restoration model. As the imaging models are usually highdimensional, the existing algorithms usually only use the ﬁrstorder information of the image. Diﬀerently, a novel numerical algorithm based on the inexact Lagrangian function is proposed in this thesis, which exploits the secondorder information to reach a superlinear convergence rate. Experiments show that the proposed algorithm is able to eﬃciently reach the solution with higher accuracy compared to the stateoftheart algorithm

4 
Investigations on models and algorithms in variational approaches for image restorationFang, Yingying 17 August 2020 (has links)
Variational methods, which have proven to be very useful to solve the illposed inverse problems, have been generating a lot of research interest in the image restoration problem. It transforms the restoration problem into the optimization of a welldesigned variational model. While the designed model is convex, the recovered image is the global solution found by an appropriate numerical algorithm and the quality of the restored image depends on the accuracy of the designed model. Thus, a lot of eﬀorts have been put to propose a more precise model that can produce a result with more pleasing visual quality. Besides, due to the high dimension and the nonsmoothness of the imaging model, an eﬃcient algorithm to ﬁnd the exact solution of the variational model, is also of the research interest, since it inﬂuences the eﬃciency of the restoration techniques in the practical applications. In this thesis, we are interested in the designing of both the variational models for image restoration problems and the numerical algorithms to solve these models. The ﬁrst objective of this thesis is to make improvements on two models for image denoising. For the multiplicative noise removal, we designed a regularizer based on the statistical property of the speckle noise, which can transform the traditional model (named by AA) into a convex one. Therefore, a global solution can be found independent of the initialization of the numerical algorithm. Moreover, the regularization term added on the AA model can help produce a sharper result. The second model is improved on the traditional ROF model by adding an edge regularization which incorporates an edge prior obtained from the observed image. Extensive experiments show that designed edge regularization has superiority to increase the texture of the recovered result and remove the staircase artifacts in the meanwhile. It is also presented that the edge regularization designed can be easily adapted into other restoration task, such as image deblurring. The second objective of this thesis is to study the numerical algorithms for a general nonsmooth imaging restoration model. As the imaging models are usually highdimensional, the existing algorithms usually only use the ﬁrstorder information of the image. Diﬀerently, a novel numerical algorithm based on the inexact Lagrangian function is proposed in this thesis, which exploits the secondorder information to reach a superlinear convergence rate. Experiments show that the proposed algorithm is able to eﬃciently reach the solution with higher accuracy compared to the stateoftheart algorithm

5 
3D reconstruction of road vehicles based on textural features from a single imageLam, Waileung, William. January 2006 (has links)
Thesis (Ph. D.)University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

6 
3D reconstruction and camera calibration from circularmotion image sequencesLi, Yan, January 2005 (has links)
Thesis (Ph. D.)University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

7 
Pragmatic image reconstruction for high resolution PET scanners /Lee, Ki Sung. January 2003 (has links)
Thesis (Ph. D.)University of Washington, 2003. / Vita. Includes bibliographical references (leaves 113124).

8 
λconnectedness and its application to image segmentation, recognition and reconstructionChen, Li January 2001 (has links)
Seismic layer segmentation, oilgas boundary surfaces recognition, and 3D volume data reconstruction are three important tasks in threedimensional seismic image processing. Geophysical and geological parameters and properties have been known to exhibit progressive changes in a layer. However, there are also times when sudden changes can occur between two layers. λconnectedness was proposed to describe such a phenomenon. Based on graph theory, λconnectedness describes the relationship among pixels in an image. It is proved that λconnectedness is an equivalence relation. That is, it can be used to partition an image into different classes and hence can be used to perform image segmentation. Using the random graph theory and λconnectivity of the image, the length of the path in a λconnected set can be estimated. In addition to this, the normal λconnected subsets preserve every path that is λconnected in the subsets. An O(nlogn) time algorithm is designed for the normal λconnected segmentation. Techniques developed are used to find objects in 2D/3D seismic images. Finding the interface between two layers or finding the boundary surfaces of an oilgas reserve is often asked. This is equivalent to finding out whether a λconnected set is an interface or surface. The problem that is raised is how to recognize a surface in digital spaces. λconnectedness is a natural and intuitive way for describing digital surfaces and digital manifolds. Fast algorithms are designed to recognize whether an arbitrary set is a digital surface. Furthermore, the classification theorem of simple surface points is deduced: there are only six classes of simple surface points in 3D digital spaces. Our definition has been proved to be equivalent to MorgenthalerRosenfeld's definition of digital surfaces in direct adjacency. Reconstruction of a surface and data volume is important to the seismic data processing. Given a set of guiding pixels, the problem of generating a λconnected (subset of image) surface is an inverted problem of λconnected segmentation. In order to simplify the fitting algorithm, gradual variation, an equivalent concept of λconnectedness, is used to preserve the continuity of the fitted surface. The key theorem, the necessary and sufficient condition for the gradually varied interpolation, has been mathematically proven. A random gradually varied surface fitting is designed, and other theoretical aspects are investigated. The concepts are used to successfully reconstruct 3D seismic real data volumes. This thesis proposes λconnectedness and its applications as applied to seismic data processing. It is used for other problems such as ionogram scaling and object tracking. It has the potential to become a general technique in image processing and computer vision applications. Concepts and knowledge from several areas in mathematics such as Set Theory, Fuzzy Set Theory, Graph Theory, Numerical Analysis, Topology, Discrete Geometry, Computational Complexity, and Algorithm Design and Analysis have been applied to the work of this thesis.

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