Mathematical analysis of super-resolution methodology

The attainment of super resolution (SR) from a sequence of degraded undersampled images could be viewed as reconstruction of the high-resolution (HR) image from a finite set of its projections on a sampling lattice. This can then be formulated as an optimization problem whose solution is obtained by minimizing a cost function. The approaches adopted and their analysis to solve the formulated optimization problem are crucial, The image acquisition scheme is important in the modeling of the degradation process. The need for model accuracy is undeniable in the attainment of SR along with the design of the algorithm whose robust implementation will produce the desired quality in the presence of model parameter uncertainty. To keep the presentation focused and of reasonable size, data acquisition with multisensors instead of, say a video camera is considered.published_or_final_versio

Reconstruction of high-resolution image from movie frames.

by Ling Kai Tung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 44-45).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.7Chapter 2 --- Fundamentals --- p.9Chapter 2.1 --- Digital image representation --- p.9Chapter 2.2 --- Motion Blur --- p.13Chapter 3 --- Methods for Solving Nonlinear Least-Squares Prob- lem --- p.15Chapter 3.1 --- Introduction --- p.15Chapter 3.2 --- Nonlinear Least-Squares Problem --- p.15Chapter 3.3 --- Gauss-Newton-Type Methods --- p.16Chapter 3.3.1 --- Gauss-Newton Method --- p.16Chapter 3.3.2 --- Damped Gauss-Newton Method --- p.17Chapter 3.4 --- Full Newton-Type Methods --- p.17Chapter 3.4.1 --- Quasi-Newton methods --- p.18Chapter 3.5 --- Constrained problems --- p.19Chapter 4 --- Reconstruction of High-Resolution Images from Movie Frames --- p.20Chapter 4.1 --- Introduction --- p.20Chapter 4.2 --- The Mathematical Model --- p.22Chapter 4.2.1 --- The Discrete Model --- p.23Chapter 4.2.2 --- Regularization --- p.24Chapter 4.3 --- Acquisition of Low-Resolution Movie Frames --- p.25Chapter 4.4 --- Experimental Results --- p.25Chapter 4.5 --- Concluding Remarks --- p.26Chapter 5 --- Constrained Total Least-Squares Computations for High-Resolution Image Reconstruction --- p.31Chapter 5.1 --- Introduction --- p.31Chapter 5.2 --- The Mathematical Model --- p.32Chapter 5.3 --- Numerical Algorithm --- p.37Chapter 5.4 --- Numerical Results --- p.39Chapter 5.5 --- Concluding Remarks --- p.39Bibliography --- p.4

Joint registration and super-resolution with omnidirectional images

This paper addresses the reconstruction of high resolution omnidirectional images from multiple low resolution images with inexact registration. When omnidirectional images from low resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform domain super-resolution problem in the spherical imaging framework. We describe how several spherical images with arbitrary rotations in the SO(3) rotation group contribute to the reconstruction of a high resolution image with help of the Spherical Fourier Transform (SFT). As low resolution images might not be perfectly registered in practice, the impact of inaccurate alignment on the transform coefficients is further analyzed. We then cast the joint registration and super-resolution problem as a total least squares norm minimization problem in the SFT domain. A l1- regularized total least squares problem is also considered. The regularized problem is solved efficiently by interior point methods. Experiments with synthetic and natural images show that the proposed scheme leads to effective reconstruction of high resolution images even when large registration errors exist in the low resolution images. The quality of the reconstructed images also increases rapidly with the number of low resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes. Finally, we highlight the benefit of the additional regularization constraint that clearly leads to reduced noise and improved reconstruction quality

3-D surface modelling of the human body and 3-D surface anthropometry

This thesis investigates three-dimensional (3-D) surface modelling of the human body and 3-D surface anthropometry. These are two separate, but closely related, areas. 3-D surface modelling is an essential technology for representing and describing the surface shape of an object on a computer. 3-D surface modelling of the human body has wide applications in engineering design, work space simulation, the clothing industry, medicine, biomechanics and animation. These applications require increasingly realistic surface models of the human body. 3-D surface anthropometry is a new interdisciplinary subject. It is defined in this thesis as the art, science, and technology of acquiring, modelling and interrogating 3-D surface data of the human body. [Continues.

Aeronautical engineering: A continuing bibliography with indexes (supplement 284)

This bibliography lists 974 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1992. The coverage includes documents on design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems. It also includes research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles