Wavelet inverse scale space for image restoration

This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.

A Closure for Isotropic Turbulence Based on Extended Scale Similarity Theory in Physical Space

The closure of a turbulence field is a longstanding fundamental problem, while most closure models are introduced in spectral space. Inspired by Chou＇s quasi-normal closure method in spectral space, we propose an analytical closure model for isotropic turbulence based on the extended scale similarity theory of the velocity structure function in physical space. The assumptions and certain approximations are justified with direct numerical simulation. The asymptotic scaling properties are reproduced by this new closure method, in comparison to the classical Batchelor model.

Research on cognitive load evaluation with subjective method in manual assembly

Subjective scales have different kinds of applicability in diverse fields.This study intends to implement a quantitative approach to determine the applicability of subjective scales in manual as-sembly work and evaluate the cognitive load of assembly workers.A multi-scale research paradigm based on subjective evaluation method is proposed.Three typical task stages are extracted from the process of assembly work.The National Aeronautics and Space Administration Task Load Index(NASA-TLX)scale,PAAS scale and Workload Profile Index Ratings(WP)scale are selected for the design of 3×3 multi-factor mixed experiment.The power spectrum density(PSD)characteris-tics of electroencephalogram(EEG)are utilized to identify the difficulty levels of the three task sta-ges.The relevant indicators of scale applicability are assessed.The results show that in terms of sensitivity,NASA-TLX scale reaches the highest sensitivity(F=999.137,P=0<0.05).In terms of validity,NASA-TLX scale possesses the best concurrent validity(P=0.0255<0.05).In terms of diagnosticity,NASA-TLX scale based on 6 dimensions takes on the best diagnostic performance.In terms of subject acceptability,WP scale performs the worst.According to the analytic hierarchy process(AHP)model,the applicability scores of NASA-TLX scale,PAAS scale and WP scale are determined as 3,2.55 and 1.6714,respectively.Therefore,NASA-TLX scale is regarded as the most suitable subjective evaluation questionnaire for assembly workers,which is also an effective quantitative evaluation method for the cognitive load of assembly workers.

Temporal Shape Error Concealment for Video Objects

A novel temporal shape error concealment technique is proposed, which can he used in the context of object-based video coding schemes. In order to reduce the effect of the shape variations of a video object, the curvature scale space （CSS） technique is adopted to extract features, and then these features are used for boundary matching between the current frame and the previous frame. Because the temporal, spatial and sta- tistical video contour information are all considered, the proposed method can find the optimal matching, which is used to replace the damaged contours. The simulation results show that the proposed algorithm achieves better subjective, objective qualities and higher efficiency than those previously developed methods.

Grid Services Trusted Authentication Algorithm Based on Image Trusted Root in Scale Space

A new authentication algorithm for grid identity trusted computing unlimited by hardware is presented;the trusted root is made as an image data.The grid entity is trusted in the soft platform when its feature of image root is entirely matched with that from the other entities＇ feature database in a scale space process.To recognize and detect the stable image root feature,the non-homogeneous linear expandable scale space is proposed.Focusing on relations between the scale parameter of the inhomogeneous Gaussian function terms and the space evolution of thermal diffusion homogeneous equations,three space evolution operators are constructed to exact and mark the feature from image root.Analysis and verification are carried on the new scale space,operators and the core of making decisions for grid entities certifications.

PIECEWISE SPARSE RECOVERY VIA PIECEWISE INVERSE SCALE SPACE ALGORITHM WITH DELETION RULE

In some applications,there are signals with piecewise structure to be recovered.In this paper,we propose a piecewise_ISS(P_ISS)method which aims to preserve the piecewise sparse structure(or the small-scaled entries)of piecewise signals.In order to avoid selecting redundant false small-scaled elements,we also implement the piecewise_ISS algorithm in parallel and distributed manners equipped with a deletion rule.Numerical experiments indicate that compared with alSS,the P_ISS algorithm is more effective and robust for piecewise sparse recovery.

An improved empirical wavelet transform method for rolling bearing fault diagnosis

Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can easily be separated,causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively.This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set.The frequency domain feature parameter set includes two characteristic parameters:mean and variance.After adaptively dividing the frequency band by the scale space method,the mean and variance of each band are calculated.Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis.An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode.The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes.The empirical mode with the largest margin factor is selected to envelope spectrum analysis.Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.

MULTISCALE MECHANICAL BEHAVIORS IN DISCRETE MATERIALS: A REVIEW

The discrete material, which belongs to the category of soft materials, is one of the most prevalent forms of matter in nature and engineering fields. These materials often exhibit abundant and complex mechanical properties which are still far from being perfectly understood. From the view of multi-scale framework concentrated on the ＇bridge＇ role in the macro-micro relation, this review mainly introduces some theoretical investigations of mechanical behaviors in discrete materials, including the continuum constitutive model based on the macroscopic phenomenological approach and coupled micro-macro approach, the statistical analysis of some microscopic physical quantities involved contacted forces between particles and its transmission within the whole system, and the statistical analysis for some microscopic processes in aeolian landform systems involving the grain-bed impact, the transportation and sedimentation of wind-blown sand flux, et al. Finally, some further worthwhile challenges in these fields are suggested.

Numerical Schemes for Linear and Non-Linear Enhancement of DW-MRI

We consider the linear and non-linear enhancement of diffusion weighted magnetic resonance images(DW-MRI)to use contextual information in denoising and inferring fiber crossings.We describe the space of DW-MRI images in a moving frame of reference,attached to fiber fragments which allows for convection-diffusion along the fibers.Because of this approach,our method is naturally able to handle crossings in data.We will perform experiments showing the ability of the enhancement to infer information about crossing structures,even in diffusion tensor images(DTI)which are incapable of representing crossings themselves.We will present a novel non-linear enhancement technique which performs better than linear methods in areas around ventricles,thereby eliminating the need for additional preprocessing steps to segment out the ventricles.We pay special attention to the details of implementation of the various numeric schemes.

A GREEDY ALGORITHM FOR SPARSE PRECISION MATRIX APPROXIMATION

Precision matrix estimation is an important problem in statistical data analysis.This paper proposes a sparse precision matrix estimation approach,based on CLIME estimator and an efficient algorithm GISSP that was originally proposed for li sparse signal recovery in compressed sensing.The asymptotic convergence rate for sparse precision matrix estimation is analyzed with respect to the new stopping criteria of the proposed GISSP algorithm.Finally,numerical comparison of GISSP with other sparse recovery algorithms,such as ADMM and HTP in three settings of precision matrix estimation is provided and the numerical results show the advantages of the proposed algorithm.

Latent Local Feature Extraction for Low-Resolution Virus Image Classification

Virus image classification is a significant and challenging issue in both clinical virology and medical image processing.Due to the low-resolution virus images in the original dataset,there is tricky difficulty in extracting useful features from this kind of poor quality images adopting the traditional feature extraction methods.In this paper,we propose an effective and robust method,which eliminates the drawbacks of traditional local feature extraction methods and conducts latent local texture feature extraction thus to promote the accuracy of virus image classification.Firstly,the multi-scale principal component analysis(PCA)filters are learned from all original images.Then,it establishes a scale space for each PCA-filtered image by 2D Gaussian function.Finally,some typical feature descriptors are employed to extract texture features from all images,which include the original image and its filtered images by PCA and Gaussian filters.Aiming at the classification of low-resolution images,the proposed method solves the difficulty in extracting the essential feature from the original image and captures its latent and principal texture information from different perspectives in different filtered images.Experimental results show that the classification accuracy of the proposed method is much higher than state-of-the-art methods in the same low-resolution virus dataset,reaching 88.00%.