Function Estimation Method Research Articles

A novel Retinex image enhancement approach via brightness channel prior and change of detail prior

In this paper, we propose a novel Retinex image enhancement approach by adopting two important prior named brightness channel prior (BCP) and change of detail (CoD) prior. We first derive a rough illumination map estimation method via BCP and Retinex model. Then, we present a combination refining method which involves the guided filter and a new total variation (TV) smoothing operator to eliminate the block effect in the rough illumination map while maintain the local smoothness property. In addition, we propose a novel sharping algorithm rely on CoD prior to improve the visual effect of the degraded image. Experimental results verify that our approach outperforms current approaches in terms of effectiveness, efficiency and universality.

Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model.

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-vascular cancer therapies. However, there is no consensus on the vascular input function estimation method, which is critical to kinetic modeling and K trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel's best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-vascular drug treatment. Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo,significant reductions in K trans and AUC occurred 2days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K trans consistent with published data in xenograft models. The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K trans results consistent with literature values and suitable for compound studies.

Depth Map Reconstruction for Underwater Kinect Camera Using Inpainting and Local Image Mode Filtering

Underwater optical cameras are widely used for security monitoring in ocean, such as earthquake prediction and tsunami alarming. Optical cameras recognize objects for autonomous underwater vehicles and provide security protection for sea-floor networks. However, there are many issues for underwater optical imaging, such as forward and backward scattering, light absorption, and sea snow. Many underwater image processing techniques have been proposed to overcome these issues. Among these techniques, the depth map gives important information for many applications of the post-processing. In this paper, we propose a Kinect-based underwater depth map estimation method that uses a captured coarse depth map by Kinect with the loss of depth information. To overcome the drawbacks of low accuracy of coarse depth maps, we propose a corresponding reconstruction architecture that uses the underwater dual channels prior dehazing model, weighted enhanced image mode filtering, and inpainting. Our proposed method considers the influence of mud sediments in water and performs better than the traditional methods. The experimental results demonstrated that, after inpainting, dehazing, and interpolation, our proposed method can create high-accuracy depth maps.

Depth Map Estimation Using Census Transform for Light Field Cameras

Depth estimation for the lense-array type cameras is a challenging problem because of sensor noise and radiometric distortion which is a global brightness change between sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against the sensor noise and the radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial map obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. We refine an initial map by the optimization which uses the assumption that the variations of the depth values in the depth map and of the pixel values in the texture-less objects are similar. Experiments show that our method outperforms the conventional methods.

On-Orbit Spatial Quality Evaluation and Image Restoration of FengYun-3C/MERSI

The Medium Resolution Spectral Imager (MERSI) was installed as a key payload on the FengYun-3C (FY-3C) polar-orbit meteorological satellite, which was successfully launched on September 23, 2013. We used 14 months of continuous FY-3C/MERSI Level-1B data (from November 2013 to December 2014) to evaluate the on-orbit image spatial quality. Based on a polar ice block image, a sharp target modulation transfer function (MTF) estimation method is used to quantitatively estimate the MTF value at the Nyquist frequency, which is an index for the spatial quality. The results show very good stability in the first year and a relatively lower spatial quality (MTF approximately 0.15) along the FY-3C/MERSI scan direction. This lower spatial quality is primarily attributed to the known and fixed 27% overlapped scan mode of MERSI, which can significantly reduce the image contrast. By using this fixed overlapped proportion (27%), we develop a fast and robust image restoration algorithm based on the Gaussian elimination (GE) method with lower and upper triangular matrix decomposition (LU). The speed-up ratio of this GE with LU decomposition method can attain a value of 626.30 compared with the traditional GE method when it solves linear equations with 2048 MERSI scan pixels. After the image restoration process, significant enhancement in the image spatial quality along the scan direction for every band of FY-3C/MERSI can be found with an increased MTF value of approximately 0.30. However, we evaluate the possible effect of this restoration algorithm on the original digital number (DN) and reflectance values. We find a slight decrease in the total averaged DN (0.5) and reflectance (< 0.5%, relative bias) values. The variation in DN or reflectance after the image restoration process exhibits a positive correlation with homogeneity of the original target. Moreover, a sensitivity study on the reflectance reveals that it has a more significant impact on the inhomogeneous pixel with a low DN value.

Estimation and variable selection in censored regression model with smoothly clipped absolute deviation penalty

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

Data classification with binary response through the Boosting algorithm and logistic regression

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is Boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that Boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the Boosting algorithm, specifically the Binomial Boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer–Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy and lower values of false positives rates and false negatives rates, making it a model with better discrimination power compared to the LRMML model. Based on these results, the logistic model adjusted via the Binomial Boosting algorithm (LRMBB model) is better suited to describe the problem of binary response, because it provides more accurate information regarding the problem considered.

Integration of colour and affine invariant feature for multi-view depth video estimation

Although many depth map estimation methods have been developed, depth map estimation still has some difficulties in low-texture regions and occlusion. This paper presents a novel approach for multi-view depth video estimation which adopts an adaptive matching scheme for high-texture regions and low-texture regions, respectively. For low-texture regions, the proposed method integrates affine invariant features with colour to make the matching more robust. Furthermore, according to the smoothness assumption of depth maps, a refinement is then performed on the estimated depth maps. Experimental results show that the proposed method can achieve better or comparable performances than the state-of-the-art method in the category of local methods even with the less running time.

Transfer function and transient estimation by Gaussian process regression in the frequency domain

Inspired by the recent promising developments of Bayesian learning techniques in the context of system identification, this paper proposes a Transfer Function estimator, based on Gaussian process regression. Contrary to existing kernel-based impulse response estimators, a frequency domain approach is adopted. This leads to a formulation and implementation which is seamlessly valid for both continuous- and discrete-time systems, and which conveniently enables the selection of the frequency band of interest. A pragmatic approach is proposed in an output error framework, from sampled input and output data. The transient is dealt with by estimating it simultaneously with the transfer function.Modelling the transfer function and the transient as Gaussian processes allows for the incorporation of relevant prior knowledge on the system, in the form of suitably designed kernels. The SS (Stable Spline) and DC (Diagonal Correlated) kernels from the literature are translated to the frequency domain, and are proven to impose the stability of the estimated transfer function. Specifically, the estimates are shown to be stable rational functions in the frequency variable. The hyperparameters of the kernel are tuned via marginal likelihood maximisation.The comparison between the proposed method and three existing methods from the literature–the regularised finite impulse response (RFIR) estimator, the Local Polynomial Method (LPM), and the Local Rational Method for Frequency Response Function estimation–is illustrated on simulations on multiple case studies.

A comparison of the variance estimation methods for heteroscedastic nonlinear models.

Heteroscedasticity is commonly encountered when fitting nonlinear regression models in practice. We discuss eight different variance estimation methods for nonlinear regression models with heterogeneous response variances, and present a simulation study to compare the performance of the eight methods in terms of estimating the standard errors of the fitted model parameters. The simulation study suggests that when the true variance is a function of the mean model, the power of the mean variance function estimation method and the transform-both-sides method are the best choices for estimating the standard errors of the estimated model parameters. In general, the wild bootstrap estimator and two modified versions of the standard sandwich variance estimator are reasonably accurate with relatively small bias, especially when the heterogeneity is nonsystematic across values of the covariate. Furthermore, we note that the two modified sandwich estimators are appealing choices in practice, considering the computational advantage of these two estimation methods relative to the variance function estimation method and the transform-both-sides approach. Copyright © 2016 John Wiley & Sons, Ltd.

Study on the Performance of the “Pendulor” Wave Energy Converter in an Array Configuration

For over three decades the “Pendulor” wave energy device has had a significant influence in this field, triggering several research endeavours. It includes a top-hinged flap propelled by the standing waves produced in a caisson with a back wall on the leeward side. However, one of the main disadvantages which impedes its progress is the enormous expense involved in the construction of the custom made typical caisson structure, about a little more than one-quarter of the wave length. In this study, the influence of such design parameters on the performance of the device is investigated, via numerical modelling for a device arranged in an array configuration, for irregular waves. The potential wave theory is applied to derive the frequency-dependent hydrodynamic parameters by making a distinction in the fluid domain into a separate sea side and lee side. The Cummins equation was utilised for the development of the time domain equation of motion while the transfer function estimation methods were used to solve the convolution integrals. Finally, the device was tested numerically for irregular wave conditions for a 50 kW class unit. It was observed that in irregular wave operating conditions, the caisson chamber length could be reduced by 40% of the value estimated for the regular waves. Besides, the device demonstrated around 80% capture efficiency for irregular waves thus allowing provision for avoiding the employment of any active control.

FRF Estimation through Sweep Milling Force Excitation (SMFE)

Inaccurate machine dynamics characterization is thought to be one of the main sources of errors in current cutting stability models. For this reason, traditional dynamic characterization procedures have been called into question. A new method for frequency response function (FRF) estimation using the real milling force as the input excitation is proposed. It consists of exciting the structure through several cutting tests at increasing or decreasing spindle speed while measuring its response. This sweep milling force excitation (SMFE) procedure allows obtaining the FRF under real cutting conditions. The results obtained have improved stability prediction with respect to conventional impact tests.

A Depth Map Post-Processing Approach Based on Adaptive Random Walk With Restart

Accurate depth estimation is still an important challenge after a decade, particularly from stereo images. The accuracy comes from a good depth level and preserved structure. For this purpose, a depth post-processing framework is proposed in this paper. The framework starts with the “Adaptive Random Walk with Restart (2015)” algorithm. To refine the depth map generated by this method, we introduced a form of median solver/filter based on the concept of the mutual structure, which refers to the structural information in both images. This filter is further enhanced by a joint filter. Next, a transformation in image domain is introduced to remove the artifacts that cause distortion in the image. The proposed post-processing method is then compared with the top eight algorithms in the Middlebury benchmark. To explore how well this method is able to compete with more widely known techniques, a comparison is performed with Google’s new depth map estimation method. The experimental results demonstrate the accuracy and efficiency of the proposed post-processing method.

A new gradient shimming method based on undistorted field map of B0 inhomogeneity

Most existing gradient shimming methods for NMR spectrometers estimate field maps that resolve B0 inhomogeneity spatially from dual gradient-echo (GRE) images acquired at different echo times. However, the distortions induced by B0 inhomogeneity that always exists in the GRE images can result in estimated field maps that are distorted in both geometry and intensity, leading to inaccurate shimming. This work proposes a new gradient shimming method based on undistorted field map of B0 inhomogeneity obtained by a more accurate field map estimation technique. Compared to the traditional field map estimation method, this new method exploits both the positive and negative polarities of the frequency encoded gradients to eliminate the distortions caused by B0 inhomogeneity in the field map. Next, the corresponding automatic post-data procedure is introduced to obtain undistorted B0 field map based on knowledge of the invariant characteristics of the B0 inhomogeneity and the variant polarity of the encoded gradient. The experimental results on both simulated and real gradient shimming tests demonstrate the high performance of this new method.

Penalized estimation equation for an extended single-index model

The single-index model is a useful extension of the linear regression model. Cui et al. (Ann Stat 39:1658–1688, 2011) proposed an estimating function method for the estimation of index vector in an extended single-index model (ESIM). Nevertheless, how to conduct variable selection for ESIM has not been studied. To solve this problem, we penalize the estimating equation with some types of penalty, such as smoothly clipped absolute deviation penalty and adaptive lasso penalty. Under some regularity conditions, the oracle property is established, i.e., the resulting estimator can be as efficient as the oracle estimator, thus we improve the explanatory ability and accuracy of estimator for the ESIM. A novel algorithm is proposed to solve the penalized estimating equation by combining quasi-Fisher scoring type algorithm and MM algorithm. Simulation study and real data application demonstrate the excellent performance of the proposed estimators.

Depth-based defocus map estimation using off-axis apertures.

This paper presents a depth-based defocus map estimation method from a single camera with multiple off-axis apertures. The proposed estimation algorithm consists of two steps: (i) object distance estimation using off-axis apertures and (ii) defocus map estimation based on the object distance. The proposed method can accurately estimate the defocus map using object distances that are well-characterized in a color shift model-based computational camera. Experimental results show that the proposed method outperforms the state-of-the-art defocus estimation methods in the sense of both accuracy and the estimation range. The proposed defocus map estimation method is suitable for multifocusing, refocusing, and extended depth of field (EDoF) systems.

On Variational and PDE‐Based Distance Function Approximations

AbstractIn this paper, we deal with the problem of computing the distance to a surface (a curve in two dimensional) and consider several distance function approximation methods which are based on solving partial differential equations (PDEs) and finding solutions to variational problems. In particular, we deal with distance function estimation methods related to the Poisson‐like equations and generalized double‐layer potentials. Our numerical experiments are backed by novel theoretical results and demonstrate efficiency of the considered PDE‐based distance function approximations.

High temporal resolution dynamic MRI and arterial input function for assessment of GFR in pediatric subjects.

To introduce a respiratory-gated high-spatiotemporal-resolution dynamic-contrast-enhanced MRI technique and a high-temporal-resolution aortic input function (HTR-AIF) estimation method for glomerular filtration rate (GFR) assessment in children. A high-spatiotemporal-resolution DCE-MRI method with view-shared reconstruction was modified to incorporate respiratory gating, and an AIF estimation method that uses a fraction of the k-space data from each respiratory period was developed (HTR-AIF). The method was validated using realistic digital phantom simulations and demonstrated on clinical subjects. The GFR estimates using HTR-AIF were compared with estimates obtained by using an AIF derived directly from the view-shared images. Digital phantom simulations showed that using the HTR-AIF technique gives more accurate AIF estimates (RMSE = 0.0932) compared with the existing estimation method (RMSE = 0.2059) that used view-sharing (VS). For simulated GFR > 27 mL/min, GFR estimation error was between 32% and 17% using view-shared AIF, whereas estimation error was less than 10% using HTR-AIF. In all clinical subjects, the HTR-AIF method resulted in higher GFR estimations than the view-shared method. The HTR-AIF method improves the accuracy of both the AIF and GFR estimates derived from the respiratory-gated acquisitions, and makes GFR estimation feasible in free-breathing pediatric subjects.

Laboratory experiment of the structural sensing method using the accurate artificial vibrator

This paper presents a structural sensing method using the accurate artificial vibrator and shows the results of vibration experiments in laboratory scale. The accurate artificial vibrator generates harmonic forces by rotating an eccentric mass. Since the rotation is accurately controlled by means of a phase control type servomotor, the vibrator generates accurate harmonic forces periodically. The phase information obtained from the servomotor let us calculate the force function precisely. Applying Auto-Regressive eXogenous (ARX) model to the time series of force function and measured displacement responses yields the Green function of a target structure. In this research, we investigated the performance of prototype system and developed the Green function estimation method. We also conducted laboratory scale vibration tests in which the prototype system was applied to a two-meter simple beam. The estimated Green function was simulated by the analysis of Finite Element Method (FEM) and its structural model was successfully determined.

Dark channel prior based blurred image restoration method using total variation and morphology

The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical applications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation (TV) and image morphology transform (specifically top-hat transform and bottom-hat transform) have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The estimated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.