做网站被骗该咋样做,广告关键词排名,百度自助网站建设,免费做代理的网站以图像处理为例#xff0c;拉普拉斯算子是基于图像的二阶导数来找到边缘并搜索过零点#xff0c;传统的拉普拉斯算子常产生双像素宽的边缘#xff0c;对于较暗区域中的亮斑进行边缘检测时#xff0c;拉普拉斯运算就会使其变得更亮。因此#xff0c;与梯度算子一样#xf…以图像处理为例拉普拉斯算子是基于图像的二阶导数来找到边缘并搜索过零点传统的拉普拉斯算子常产生双像素宽的边缘对于较暗区域中的亮斑进行边缘检测时拉普拉斯运算就会使其变得更亮。因此与梯度算子一样拉普拉斯算子不能抑制图像的噪声。如果有一种算子能够将高斯平滑滤波器与拉普拉斯算子结合起来在进行边缘检测之前先平滑掉噪声一定能够取得良好的效果因此高斯—拉普拉斯算子被提出。鉴于此采用一种改进的高斯-拉普拉斯滤波器对一维时间序列进行平滑与降噪运行环境为MATLAB R2018测试了地震信号微震信号。
clc
clear
close all
%load Input example
load mport1-1.txt
xmport1_1;
% Sigma and Filter Order
% Tune sigma, and N according to the application you want to use.
sigma 0.5;
N 10;
% For More Scaling dividing by sigma.
[Gaussian_1D_2_Diff_Modified]IGLF(sigma,N) /sigma;
% Filtering The Input (Denoising the Input Signal)
Output filter (Gaussian_1D_2_Diff_Modified,1,x);
% Output Squaring
Output Output.^2;
% For more smoothing, average movabale window is obatined for m samples
% with k stride (moving by k samples)
% Adjustce m, and k according to the application you want to use.
Output_More_Smoothing zeros(1,size(x,2));
m 8;
k 1;
count 1;for j1:k:(length(Output))-max([m k])Output_More_Smoothing(count)(mean(Output(j:jm-1)));count count 1;end% Plotting
% Plotting the Cofficients of MLOG.
figure(1)
plot( (1:N), Gaussian_1D_2_Diff_Modified, LineWidth,3)
xlabel(Index (n),FontSize,14,FontWeight,bold)
ylabel(MLOG Mask Values,FontSize,14,FontWeight,bold)
title(MLOG MASK,FontSize,14,FontWeight,bold)
set(gca,fontsize,12,FontWeight,bold)
% Plotting Input, Output of MLOG, and Smoothed Output Signal.
figure(2)
subplot(3,1,1),plot(1:length(x),x)
xlabel(Sample Index,FontSize,14,FontWeight,bold)
ylabel(Count,FontSize,14,FontWeight,bold)
title(Input Signal,FontSize,14,FontWeight,bold)
set(gca,fontsize,12,FontWeight,bold)
subplot(3,1,2),plot(1:length(Output), Output)
xlabel(Sample Index,FontSize,14,FontWeight,bold)
ylabel(Count^2,FontSize,14,FontWeight,bold)
title(Output Signal of IGLF Filter,FontSize,14,FontWeight,bold)
set(gca,fontsize,12,FontWeight,bold)
subplot(3,1,3),plot(1:length(Output_More_Smoothing), Output_More_Smoothing)
xlabel(Sample Index,FontSize,14,FontWeight,bold)
ylabel(Count^2,FontSize,14,FontWeight,bold)
title(Smoothed Output Signal,FontSize,14,FontWeight,bold)
set(gca,fontsize,12,FontWeight,bold)知乎学术咨询
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