Quantum color image scaling based on bilinear interpolation

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2^(n_(1)) × 2^(n_(2)) quantum color image scaling. Firstly, the improved novel quantum representation of color digital images(INCQI) is employed to represent a 2^(n_(1)) × 2^(n_(2)) quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed.Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.

A theoretical study of the multigrid three-dimensional variational data assimilation scheme using a simple bilinear interpolation algorithm

In order to solve the so-called ＂bull-eye＂ problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational （3DVAR） data assimilation scheme, a smoothing term, equivalent to a penalty term, is introduced into the cost function to serve as a means of troubleshooting. A theoretical analysis is first performed to figure out what on earth results in the issue of ＂bull-eye＂, and then the meaning of such smoothing term is elucidated and the uniqueness of solution of the multigrid 3DVAR with the smoothing term added is discussed through the theoretical deduction for one-dimensional （1D） case, and two idealized data assimilation experiments （one- and two-dimensional （2D） cases）. By exploring the relationship between the smoothing term and the recursive filter theoretically and practically, it is revealed why satisfied analysis results can be achieved by using such proposed solution for the issue of the multigrid 3DVAR.

Using WGM2012 to Compute Gravity Anomaly Corrections of Leveling Observations in China

Gravity Anomaly Correction(GAC)is a very important term in leveling data processing.In most cases,it is troublesome for field surveyors to measure gravity when leveling.In this paper,based on the complete Bouguer Gravity Anomaly(BGA)map of WGM2012,the feasibility of replacing in-situ gravity surveying in China is investigated.For leveling application,that is to evaluate the accuracy of WGM2012 in China.Because WGM2012 is organized with a standard rectangle grid,two interpolation methods,bilinear interpolating and Inverse Distance Weighted(IDW)interpolating,are proposed.Four sample areas in China,i.e.,Hanzhong,Chengdu,Linzhi and Shantou,are selected to evaluate the systems bias and precision of WGM2012.Numerical results show the average system bias of WGM2012 BGA in west China is about-100.1 mGal(1 mGal=10^(-5) m/s^(2))and the standard deviation is about 30.7 mGal.Tests in Shantou indicate the system bias in plain areas is about-130.4 mGal and standard deviation is about 6.8 mGal.All these experiments means the accuracy of WGM2012 is limited in high mountain areas of western China,but in plain areas,such as Shantou,WGM2012 BGA map is quite good for most leveling applications after calibrating the system bias.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

An image compressed sensing algorithm based on adaptive nonlinear network

Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured values.The computation is complex and the reconstruction time is long.The deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time,but the algorithm emphasis is placed on reconstructing the network part mostly.The random measurement matrix cannot measure the image features well,which leads the reconstructed image quality to be improved limitedly.Two kinds of networks are proposed for solving this problem.The first one is ReconNet’s improved network IReconNet,which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement network.The reconstruction quality and anti-noise performance are greatly improved.Because the measured values extracted by the measurement network also retain the characteristics of image spatial information,the image is reconstructed by bilinear interpolation algorithm(Bilinear)and dilate convolution.Therefore a second network USDCNN is proposed.On the BSD500 dataset,the sampling rates are 0.25,0.10,0.04,and 0.01,the average peak signal-noise ratio(PSNR)of USDCNN is 1.62 dB,1.31 dB,1.47 dB,and 1.95 dB higher than that of MSRNet.Experiments show the average reconstruction time of USDCNN is 0.2705 s,0.3671 s,0.3602 s,and 0.3929 s faster than that of ReconNet.Moreover,there is also a great advantage in anti-noise performance.

Study on the vehicle license plate tilt correction

The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correction in the horizontal direction and the vertical direction Owing to the VLP tilt taking place in the vertical direction,the array of the image’s pixels of the same column is broken,and even different rows come into being superposition.The VLP tilt taking place in the horizontal direction,by which the array of the image’s pixels of the same row broken,and so much as different columns come into being superposition.

A Finite-Difference Approximation for the Oneand Two-Dimensional Tempered Fractional Laplacian

This paper provides a finite-difference discretization for the one-and two-dimensional tempered fractional Laplacian and solves the tempered fractional Poisson equation with homogeneous Dirichlet boundary conditions.The main ideas are to,respectively,use linear and quadratic interpolations to approximate the singularity and non-singularity of the one-dimensional tempered fractional Laplacian and bilinear and biquadratic interpolations to the two-dimensional tempered fractional Laplacian.Then,we give the truncation errors and prove the convergence.Numerical experiments verify the convergence rates of the order O(h^2−2s).

Fast bowtie effect elimination for MODIS L1B data

The bowtie effect refers to geometry distortions for the moderate resolution imaging spectrum-radiometer （MODIS） level 1B （L1 B） data. Till now, to eliminate the bowtie effect, numerous methods are proposed. However, most of them have limitations in computation efficiency. Through a comparative study of existing methods, this article puts forward a fast method to eliminate the bowtie effect using the ephemeris data. In this method, the rough positions of overlapping data are first detected. Because of the influence caused by the instrarnent characters and the earth＇s curvature, the positions of overlapping data need to be rectified to obtain more precise results. The optimal rectification method used in this article is selected by comparing three methods. By using the optimal method, the rectified MODIS data can be obtained. The experiments demonstrate that the bowtie effect can be eliminated in less than 1 s. In contrast, other traditional methods spend at least 3 s, thus the proposed method is faster and more effective.