Improvement of Binocular Reconstruction Algorithm for Measuring 3D Pavement Texture Using a Single Laser Line Scanning Constraint

The dense and accurate measurement of 3D texture is helpful in evaluating the pavement function.To form dense mandatory constraints and improve matching accuracy,the traditional binocular reconstruction technology was improved threefold.First,a single moving laser line was introduced to carry out global scanning constraints on the target,which would well overcome the difficulty of installing and recognizing excessive laser lines.Second,four kinds of improved algorithms,namely,disparity replacement,superposition synthesis,subregion segmentation,and subregion segmentation centroid enhancement,were established based on different constraint mechanism.Last,the improved binocular reconstruction test device was developed to realize the dual functions of 3D texture measurement and precision self-evaluation.Results show that compared with traditional algorithms,the introduction of a single laser line scanning constraint is helpful in improving the measurement’s accuracy.Among various improved algorithms,the improvement effect of the subregion segmentation centroid enhancement method is the best.It has a good effect on both overall measurement and single pointmeasurement,which can be considered to be used in pavement function evaluation.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.

Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm

Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a highly ill-posed problem to fulfll 3D reconstruction inFMT.In this contribution,we propose an efficient iterative algorithm to solve the large-scalereconstruction problem,in which the sparsity of fluorescent targets is taken as useful a prioriinformation in designing the reconstruction algorithm.In the implementation,a fast sparseapproximation scheme combined with a stage-wise learning strategy enable the algorithm to dealwith the ill-posed inverse problem at reduced computational costs.We validate the proposed fastiterative method with numerical simulation on a digital mouse model.Experimental results demonstrate that our method is robust for different finite element meshes and different Poissonnoise levels.

Source reconstruction for bioluminescence tomography via L_(1/2)regularization

Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem.The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm.In this paper,we present a reconstruction method based on L_(1/2) regularization to enhance sparsity of BLT solution and solve the nonconvex L_(1/2) norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights.To assess the performance of the proposed reconstruction algorithm,simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms,including the weighted interior-point,L1 homotopy,and the Stagewise Orthogonal Matching Pursuit algorithm.Simulation results show that the proposed method yield stable reconstruction results under different noise levels.Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy,multiple-source resolving and image quality.

Simulation and photoelectron track reconstruction of soft X-ray polarimeter

The soft X-ray polarimeter(SXP)is a detector with a wide energy range,large area,and large field of view.A SXP will be mounted on the Chinese Space Station and will mainly focus on detecting the polarization of transient soft X-ray(2–10 keV)sources,especially gamma-ray bursts(GRBs).In this work,a polarimeter detector unit is taken as an example,and Geant4 and Garfield++software are used to simulate the detection efficiency and track production.An improved track reconstruction algorithm is proposed and used to reconstruct two-dimensional images of the tracks.In this method,the initial emission angle of photoelectrons is reconstructed from the initial part of the track by shortening or extending the initial part of the track until the remaining track is straight,and the number of pixels is within an adjustable threshold.The modulation factor of the photoelectronic tracks after reconstruction reaches approximately 57%in the photon energy range of 7–10 keV.

Image Reconstruction Based on Compressed Sensing Measurement Matrix Optimization Method

In this paper,the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied.The advantages and disadvantages of greedy reconstruction algorithm are analyzed.The disadvantages of signal sparsely are preset in this algorithm.The sparsely adaptive estimation algorithm is proposed.The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve.The sparse step size adaptive compressed sampling matching tracking algorithm is proposed.The improved algorithm selects the sparsely as the step size to select the support set atom,and the maximum correlation value.Half of the threshold culling algorithm supports the concentration of excess atoms.The experimental results show that the improved algorithm has better power and lower image reconstruction error under the same sparsely criterion,and has higher image reconstruction quality and visual effects.

Video Compressed Sensing Reconstruction Based on Multi-Dimensional Reference Frame Multi Hypothesis Rediction

In this paper,a video compressed sensing reconstruction algorithm based on multidimensional reference frames is proposed using the sparse characteristics of video signals in different sparse representation domains.First,the overall structure of the proposed video compressed sensing algorithm is introduced in this paper.The paper adopts a multi-reference frame bidirectional prediction hypothesis optimization algorithm.Then,the paper proposes a reconstruction method for CS frames at the re-decoding end.In addition to using key frames of each GOP reconstructed in the time domain as reference frames for reconstructing CS frames,half-pixel reference frames and scaled reference frames in the pixel domain are also used as CS frames.Reference frames of CS frames are used to obtain higher quality assumptions.Themethod of obtaining reference frames in the pixel domain is also discussed in detail in this paper.Finally,the reconstruction algorithm proposed in this paper is compared with video compression algorithms in the literature that have better reconstruction results.Experiments show that the algorithm has better performance than the best multi-reference frame video compression sensing algorithm and can effectively improve the quality of slowmotion video reconstruction.

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms.The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme.The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor,which contains the Bayesian information.One of the extended algorithms can be applied to emission tomography and another to transmission tomography.Computer simulations are performed and compared with the corresponding un-extended algorithms.The total-variation norm is employed as the Bayesian constraint in the computer simulations.The newly developed algorithms demonstrate a stable performance.A simple Bayesian algorithm can be derived for any noise variance function.The proposed algorithms have properties such as multiplicative updating,non-negativity,faster convergence rates for bright objects,and ease of implementation.Our algorithms are inspired by Green’s one-steplate algorithm.If written in additive-update form,Green’s algorithm has a step size determined by the future image value,which is an undesirable feature that our algorithms do not have.

AN IMPROVED SPARSITY ADAPTIVE MATCHING PURSUIT ALGORITHM FOR COMPRESSIVE SENSING BASED ON REGULARIZED BACKTRACKING

Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.

Solid Modeling for Conceptual Product Design

A new theory called default geometric reasoning is put forward, and the corresponding solid reconstruction algorithm is studied. The theory and the algorithms are further verified by a sample reconstitution.

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

The present work describes the use of noninvasive diffuse optical tomography(DOT)technology to measure hemodynamic changes,providing relevant information which helps to understand the basis of neurophysiology in the human brain.Advantages such as portability,direct measurements of hemoglobin state,temporal resolution,non-restricted movements as occurs in magnetic resonance imaging(MRI)devices mean that DOT technology can be used in research and clinical fields.In this review we covered the neurophysiology,physical principles underlying optical imaging during tissue-light interactions,and technology commonly used during the construction of a DOT device including the source-detector requirements to improve the image quality.DOT provides 3 D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head.Moreover,we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing,avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.

Continuous-wave terahertz diffraction tomography for measuring three-dimensional refractive index maps

Three-dimensional(3D)refractive index(RI)distribution is important to reveal the object’s inner structure.We implemented terahertz(THz)diffraction tomography with a continuous-wave single-frequency THz source for measuring 3D RI maps.The off-axis holographic interference configuration was employed to obtain the quantitative scattered field of the object under each rotation angle.The 3D reconstruction algorithm adopted the filtered backpropagation method,which can theoretically calculate the exact scattering potential from the measured scattered field.Based on the Rytov approximation,the 3D RI distribution of polystyrene foam spheres was achieved with high fidelity,which verified the feasibility of the proposed method.

Extending the global-direction stencil with“face-area-weighted centroid”to unstructured finite volume discretization from integral formfinitevolumediscretization from integral form

Accuracy of unstructured finite volume discretization is greatly influenced by the gradient reconstruction.For the commonly used k-exact reconstruction method,the cell centroid is always chosen as the reference point to formulate the reconstructed function.But in some practical problems,such as the boundary layer,cells in this area are always set with high aspect ratio to improve the local field resolution,and if geometric centroid is still utilized for the spatial discretization,the severe grid skewness cannot be avoided,which is adverse to the numerical performance of unstructured finite volume solver.In previous work[Kong,et al.Chin Phys B 29(10):100203,2020],we explored a novel global-direction stencil and combined it with the face-area-weighted centroid on unstructured finite volume methods from differential form to realize the skewness reduction and a better reflection of flow anisotropy.Greatly inspired by the differential form,in this research,we demonstrate that it is also feasible to extend this novel method to the unstructured finite volume discretization from integral form on both second and third-order finite volume solver.Numerical examples governed by linear convective,Euler and Laplacian equations are utilized to examine the correctness as well as effectiveness of this extension.Compared with traditional vertex-neighbor and face-neighbor stencils based on the geometric centroid,the grid skewness is almost eliminated and computational accuracy as well as convergence rate is greatly improved by the global-direction stencil with face-area-weighted centroid.As a result,on unstructured finite volume discretization from integral form,the method also has superiorities on both computational accuracy and convergence rate.

On the Local Solvability and Stability for the Inverse Spectral Problem of the Generalized Dirichlet–Regge Problem

For the generalized Dirichlet–Regge problem with complex coefficients,we prove the local solvability and stability for the inverse spectral problem,which indicates an improved result of the previous work([Journal of Geometry and Physics,159,103936(2021)]).

A new three-dimensional elastography using phase based shifted Fourier transform

Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues.The aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform(SFT)to perform a quasi-static elastography.Our innovative idea is implementation of a 3D convolution instead of using three 2D convulsions.At first,we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor tissue.The primary raw data consists of a number of points with x,y and z specified for tumor and healthy breast tissue.At this step,we simulated the displacements in directions of x,y and z at each point of the prescribed tissues for 15 mm displacement of probe in–Y direction then we collected 1831 points for tumor and 4186 points for breast before and after pressure.After applying a novel reconstruction algorithm,we convolved all images with the 3D Gabor filters to obtain phases,represented displacements of the breast and tumor images for before and after pressure.To reach this goal,we designed a Gabor filter bank based on the dimensions of the input images in different scales,directions,and deviations.Using the 3D SFT,we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the images.Finally,we implemented a 2D analysis of SFT in order to investigate validation of the 3D SFT.In 2D algorithm,we used three two-dimensional convulsions in XY,YZ and XZ planes.The results obtained from the small displacements marked by circles,confirmed the accuracy of the 3D SFT algorithm.These areas of interest are the tumor areas in the 2D analysis.