Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research. In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques. In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images

A Fast Dynamic Range Compression with Local Contrast Preservation Algorithm for Low Dynamic Range Image Enhancement

[[abstract]]This paper presents a new fast dynamic range compression format with a local-contrast-preservation (FDRCLCP) algorithm to efficiently resolve low dynamic range (LDR) image enhancement problem for natural color images. The proposed FDRCLCP algorithm can combine with any continuously differentiable intensity transfer function to achieve LDR image enhancement. In combination with the FDRCLCP algorithm, a new intensity-transfer function that achieves satisfactory dynamic-range compression while preventing over enhancement in dark regions of the image is proposed. Experimental results validate that the proposed method provides better visual representation in comparison with two existing methods.[[sponsorship]]IEEE Hong Kong Section; IEEE Xi'an Section[[conferencetype]]國際[[conferencedate]]20120812~20120815[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]香

A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras

[[abstract]]This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.[[incitationindex]]SCI[[booktype]]電子

Non-implementation of property rating practice, any impact on community healthcare in Bauchi Metropolis Nigeria?

The practice of rating real estate is essentially an internal revenue source, synonymous to tenement tax levied on the owner/occupier. Property rating in Nigeria is bedevilled by many factors that impeded its smooth implementation and operation, thus, this form of taxation yields zero revenue in Bauchi, due to failure of implementation. This study is aimed at measuring the impact of non-implementation of property rating on community healthcare in Bauchi metropolis of Nigeria. Two hundred and fifty (250) closed-ended questionnaires composed in five-level Likert scale were distributed to professionals in the field of real estate and facilities management, in the academia and estate firms, and two hundred and twenty one questionnaires (221) were mailed back for analysis. The Structural Equation Modelling (SEM) in IBM version of SPSS with AMOS was used to establish relationship between the variables. Findings from this study reveals that PRP does not command direct impact on community healthcare services, however, the services financed by property rating in the area of sanitation and sewage cleaning has the tendencies to curb the occurrence of diseases like cholera and malaria. Thus, it can be understood that a fully institutionalized practice of property rating could avert the outbreak of diseases

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE