Iterative regularization method for image denoising with adaptive scale parameter

In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.

Modal Wave Number Spectrum for Mesoscale Eddies

The variations of ocean environmental parameters invariably result in variations of local modal wave numbers of a sound pressure field. The asymptotic Hankel transform with a short sliding window is applied to the complex sound pressure field in the water containing a mesoscale eddy to examine the variation of local modal wave numbers in such a range-dependent environment. The numerical simulation results show that modal wave number spectra obtained by this method can reflect the location and strength of a mesoscale eddy, therefore it can be used to monitor the strength and spatial scale of ocean mesoscale eddies.

Empirical likelihood for right censored data with covariables

For complete observation and p-dimensional parameter θ defined by an estimation equation, empirical likelihood method of construction of confidence region is based on the asymptotic χp2 distribution of -2 log（EL ratio）. For right censored lifetime data with covariables, however, it is shown in literature that -2 log（EL ratio） converges weakly to a scaled χp2 distribution, where the scale parameter is a function of unknown asymptotic covariance matrix. The construction of confidence region requires estimation of this scale parameter. In this paper, by using influence functions in the estimating equation, we show that -2 log（EL ratio） converges weakly to a standard χp2 distribution and hence eliminates the procedure of estimating the scale parameter.

Global shape reconstruction of nano grid with singly fixed camera

Depth from defocus(DFD),as a typical shape reconstruction method,has been widely researched in most recent years.However,all the existing DFD algorithms require at least two defocused images with different camera parameters.Unfortunately,in micro/nano manipulation,any change on visual sensor's parameters is absolutely forbidden.Therefore,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper.First,the blurring imaging model is constructed with the relative blurring and the diffusion equation.Second,the relationship between depth and blurring is discussed from four aspects.Subsequently,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm.Finally,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.

Semileptonic Decays B_c~*→η_cll with QCD Sum Rules

In this article, we calculate the B＊→ηc form-factors with the three-point QCD sum rules, then study the semileptonic decays B＊→ηclυl. The tiny decay widths may be observed experimentally in the future at the LHCb, while the B＊→ηc form-factors can be taken as basic input parameters in other phenomenologieal analysis.