An Optimal Factor Analysis Approach to Improve the Wavelet-based Image Resolution Enhancement Techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

An edge detection method using outer totalistic cellular automata

A number of Cellular Automata (CA)-based edge detectors have been developed recently due to the simplicity of the model and the potential for simultaneous removal of different types of noise in the process of detection. This paper introduced a novel edge detector using Outer Totalistic Cellular Automata. Its performance has been compared with other recently developed CA-based edge detectors, in addition to some classic methods, through testing images from a public library. Visual and quantitative measurement of similarity with manually marked correct edges confirmed the superiority of the proposed method over conventional and state-of-the-art CA-based edge detectors

An evaluation method for multiview surface reconstruction algorithms

We propose a new method...

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on 8 publicly available datasets (six 2D datasets, one 3D dataset and one 3D synthetic dataset) demonstrate its superior performance to other state-ofthe- art methods

Are cities healthy? A city health diagnose framework from the perspective of living organism

Cities, as organisms, exhibit complicated phenomena of life characteristics. With global urbanisation, sustaining the health status of cities has become an imperative issue. Proper diagnose upon the healthy status of cities requires a set of scientific and rigorous city health diagnose toolkits. With regard to city health diagnosis, extant studies have proposed various indicators. Whilst the diagnosis indicators appear to be fragmented and lack an organism perspective in investigating urban health status. This study thus fulfills the gap of existing literature by treating cities as living organisms and establish a conceptual framework of city living organism, which is composed by five aspects of living characteristics, including metabolism, response, adaptivity, growth, and reproduction. Based on the city organism framework, a set of city somatic index system is proposed, and detailed guidance upon how to use this set of index system to conduct city health diagnose is provided. City health diagnosis in referring to the established conceptual framework and index system can be undertaken at three levels: macro, meso and micro. The validity of the established city health diagnosis framework is tested via a case study of 12 Chinese cities. The city organism framework and city health diagnose index system provide a scientific toolkit for city managers and researchers to systematically investigate city health status and address any underlying urban diseases.</p