Local Mean Estimation Research Articles

Image quality optimization towards lidar registration based on iterative termination

Image quality optimization is a key technique in image processing, whose goal is to improve image quality by image enhancement or image format transform. This paper aims at optimizing image acquisition using Lidar registration, which can cope with disadvantages of conventional algorithms such as low-resolution. Specifically, we propose an iterative termination optimization strategy based on image quality perception features and local mean estimation. First, fuzzy images with different types and degrees of distortion are incorporated to form a representative natural image set, and feature maps of fuzzy images are extracted by the natural scene statistical method in the spatial domain. Noticeably, the proposed algorithm which performs iterative deblurring operation records the optimal iteration point based on recording the quality value FSIM of the restored image, and calibrates the corresponding feature vector in the sample library with the optimal iteration point (step number). Afterwards, we leverage LME method to implement an estimate of the number of iteration steps. Based on these two steps, the estimation of the initial iterative monitoring point is completed, so that the subsequent adaptive iterative termination work is more purposeful to monitor the defuzzification metric. The optimization operation can be completed faster effectively.

FPGA-Based Real-Time Implementation of Bivariate Empirical Mode Decomposition

A field programmable gate array (FPGA)-based parallel architecture for the real-time and online implementation of the bivariate extension of the empirical mode decomposition (EMD) algorithm is presented. Multivariate extensions of EMD have attracted significant attention in recent years owing to their scope in applications involving multichannel and multidimensional data processing, e.g. biomedical engineering, condition monitoring, image fusion. However, these algorithms are computationally expensive due to the empirical and data-driven nature of these methods. That has hindered the utilisation of EMD, and particularly its bivariate and multivariate extensions, in real-time applications. The proposed parallel architecture is aimed at bridging this gap through real-time computation of the bivariate EMD algorithm. The crux of the architecture is the simultaneous computation of multiple signal projections, locating their local extrema and finally the calculation of their associated complex-valued envelopes for the estimation of local mean. The architecture is implemented on a Xilinx Kintex 7 FPGA and offers significant computational improvements over the existing software-based sequential implementations of bivariate EMD.

Dynamically sampled multivariate empirical mode decomposition

A method for accurate multivariate local mean estimation in the multivariate empirical mode decomposition algorithm by using a statistical data-driven approach based on the Menger curvature measure and normal-to-anything variate-generation method is proposed. This is achieved by aligning the projection vectors in the direction of the maximum ‘activity’ of the input signal by considering the local curvature of the signal in multidimensional spaces, resulting in accurate mean estimation even for a very small number of projection vectors.

Practical signal-dependent noise parameter estimation from a single noisy image.

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

Dynamically-Sampled Bivariate Empirical Mode Decomposition

A novel scheme for selecting projection vectors in bivariate empirical mode decomposition (BEMD) is proposed in order to enable accurate signal decomposition at lower computational complexity. Unlike existing algorithms which use a static uniform scheme for the distribution of projection vectors, the proposed scheme examines local curvature in multidimensional spaces to produce a data-adaptive set of direction vectors for taking signal projections. This is achieved by aligning the density of projection vectors according to the empirical distributions of angles where the signal exhibits highest local dynamics. We show that the proposed methodology outperforms the existing schemes for a small number of signal projections. The proposed algorithm is verified via illustrative simulations demonstrating accurate local mean estimation and mode extraction.

Estimation of environmental optima and tolerances of diatoms using multifactor multiplicative modeling

We demonstrated the use of a multifactor multiplicative regression model to study the ecology of diatoms. A dataset of 174 samples of diatoms matched with environmental variables, which were collected from similar typology streams of three major watersheds in Ethiopia was used to build the model. Tropical rivers experience wet and dry season dynamics with highest levels of pollution at low water levels. In extreme polluted sites, diatoms are still present whereas other bio-indicator groups such as fishes, macrophytes and even macro-invertebrates are absent. The distribution of selected model diatom taxa (widely known pollution tolerant and sensitive taxa) in relation to environmental descriptors was studied by fitting non-parametric multiplicative regression (NPMR) to species relative abundances. A local mean estimator and Gaussian kernel functions were used to construct the models. The models reported here are those showing the best fit for particular number of predictors. Predictors were added until the cross-validated coefficient of multiple determination (×R2) increased at least by 5% so that the model with the highest number of predictors had the highest ×R2. We used bootstrap sampling to validate the model. NPMR a powerful model to study autoecology allows us to question the assumption that a suitable indicator species exhibits approximately bell-shaped curve with a single optimum for the probability density function of a species along environmental gradients. Only a few number of taxa showed such an optima whereas multi-interaction of diatom species could cause linear, skewed or non-unimodal, bimodal and multimodal responses. Therefore, modeling of species response to environmental gradients can be best explored without predefining the response curve and considering simultaneous multiple factor interaction in a multiplicative way.

An Improved Motion-Compensated 3-D LLMMSE Filter With Spatio–Temporal Adaptive Filtering Support

In this paper, we propose a motion-compensated spatio-temporal Locally adaptive linear minimum mean squared-error (LLMMSE) filter for noisy image sequences with both temporal and spatially adaptive filtering support. Motion compensation and an adaptive temporal filtering support (TFS) structure guarantee the uniformity of the TFS. An intelligent pixel aggregation algorithm is proposed to include homogeneous neighboring pixels and exclude the outlier pixels, resulting in uniform spatial filtering support. By using the proposed spatio-temporal LLMMSE filter with uniform spatio-temporal support, we can reduce noise efficiently without introducing visually disturbing blurring artifacts. Furthermore, we employ an adaptive weighted local mean and variance estimation algorithm to improve the accuracy of estimation. The weights provide an implicit mechanism for deemphasizing the contribution of the outlier pixels which are wrongly kept within the support to avoid blurring. The performance of the proposed filter is quantitatively evaluated and compared with state-of-the-art methods. The results demonstrate that the proposed filter can achieve superior filtering performance, both subjectively and objectively.

Dynamic shadow-power estimation for wireless communications

We present a sequential Bayesian method for dynamic estimation and prediction of local mean (shadow) powers from instantaneous signal powers in composite fading-shadowing wireless communication channels. We adopt a Nakagami-m fading model for the instantaneous signal powers and a first-order autoregressive [AR(1)] model for the shadow process in decibels. The proposed dynamic method approximates predictive shadow-power densities using a Gaussian distribution. We also derive Crame/spl acute/r-Rao bounds (CRBs) for stationary lognormal shadow powers and develop methods for estimating the AR model parameters. Numerical simulations demonstrate the performance of the proposed methods.

Polarization Diversity Measurements and Analysis for Antenna Configurations at 1800 MHz

In wireless communication systems, multipath interference has a significant impact on system design and performance. Fast fading variations are caused by the coherent summation of multiple echoes from many reflection points reaching the receive antenna. Antenna diversity is one technique that can be used to overcome multipath fading. A test system used to measure the diversity performance of an antenna pair was used to experimentally determine the complex correlation coefficient between the two antenna branches. A local mean estimation algorithm based on the channel mean square error equalization was implemented. Thus, the two parameters that determine the expected diversity gain, i.e., the complex correlation coefficient and the mean level signal difference, were estimated. The test system was used to evaluate the polarization diversity performance of different antenna pairs in Rayleigh and Rician environments, both in the absence and in the presence of a human head phantom.

A general framework for quadratic Volterra filters for edge enhancement

An inherent problem in most image enhancement schemes is the amplification of noise, which, due to Weber's law, is mostly visible in the darker portions of an image. Using a special class of quadratic Volterra filters, we can adapt the enhancement process in a computationally efficient way to the local image brightness because these filters are approximately equivalent to the product of a local mean estimator and a highpass filter. We analyze and derive this subclass of quadratic Volterra filters by investigating the 1-D case first, and then we generalize the results to two dimensions. An important property of these filters is that they map sinusoidal inputs to constant outputs, which allows us to develop a new filter characterization that is more intuitive for our application than the 4-D frequency response. This description finally leads to a novel least-squares design methodology. Image enhancement results using our Volterra filters are superior to those obtained with standard linear filters, which we demonstrate both quantitatively and qualitatively.

Shrinking a wet deposition network

Suppose that we must delete stations from a monitoring network. Which stations should be deleted if we wish the remaining network to have the smallest possible trend estimate variances? We use the spatial-temporal model described in Oehlert (1993, J. Am. Statist. Assoc., 88, 390–399), to model concentration of sulfate in wet deposition. Based on this model and three criteria, we choose good sets of candidate stations for deletion from the NADP/NTN network. We use the criteria: that the sum of 11 regional trend estimate variances be as small as possible, that the sum of local trend estimation variance be as small as possible, and that the sum of local mean estimation variance be as small as possible. Good choices of stations for deletion result in a modest increase in criteria (about 7 to 34%) for 100 stations deleted from the network, while random sets of 100 stations can increase criteria by a factor of two or more.

Velocity adaptive handoff algorithms for microcellular systems

Fast velocity adaptive handoff algorithms for microcellular systems capable of providing consistent and near optimal performance are presented and characterized. Guidelines are first developed for the amount of spatial averaging required for local mean estimation in microcellular systems. Afterwards, three different velocity estimators are introduced and compared with respect to their sensitivity to Rice factors, non-isotropic scattering, additive Gaussian noise, and performance in a adaptive handoff algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Two-dimensional LMS adaptive filter incorporating a local-mean estimator for image processing

An adaptive scheme is proposed whereby a local-mean estimation substructure is incorporated to overcome the problem of the 2-D least-mean-square (LMS) adaptive filter in dealing with signals having a nonzero mean, especially with abrupt variations (edges), which is a typical feature of practical images. The advantages of this scheme over the direct use of the 2-D LMS are considered. As a direct application, the scheme is used for adaptive cancellation of an artifact disturbance in an image. A method of implementing the local-mean estimation substructure with the edge-preserving property is introduced. Using this local-mean estimator, the scheme is applied to enhancement of images disturbed by wideband noise. All of the application examples illustrate some useful feature of the 2-D LMS adaptive filter in image processing and the improved performance of the scheme.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Adaptive filtering incorporating a local mean estimation substructure

Adaptive filters have been used successfully in many applications of signal processing. However, their performance in dealing with signals of nonzero mean, especially with sharp changes (edges), is problematic, which limits the extension of the use of adaptive filters in some important application areas. To overcome this problem, we introduce in this paper a scheme for adaptive filtering obtained by incorporating a local mean estimation substructure (denoted as AF–LME scheme). It is shown by theoretical analysis that, by handling the signal mean and the zero mean component (the residual signal) separately, the performance of adaptive algorithms (e.g. the LMS or the RLS) can be improved. Analysis is also given to show the weakness of an adaptive filter in dealing with the edges of the signal mean. This weakness can be overcome by incorporating the technique of low-pass filtering with an edge preserving property as the local mean substructure. A method for the implementation of this substructure is proposed. This implementation was satisfactorily used in our computer simulations and representative examples simulations are presented.

Disjunctive kriging, universal kriging, or no kriging: Small sample results with simulated fields

This paper provides a comparison between linear (universal) and nonlinear (disjunctive) kriging estimators when they are computed from small samples chosen randomly on simulated stationary and nonstationary fields. Point estimation results are reported. In all cases considered, kriging estimators were found better than a local mean estimator, with universal kriging either better than or as good as disjunctive kriging. The latter, which is suited to handle stationary fields, did not provide more accurate estimates because the use of small samples led to inconsistencies in the assumed bivariate model. Universal kriging was particularly better with nonstationary fields.