如何在Matlab中制作高斯滤波器(How to make a Gaussian filter in Matlab)
我试图在Matlab中制作高斯滤波器,而不使用imfilter()和fspecial() 。 我试过这个,但结果并不像我用imfilter和fspecial那样。
这是我的代码。
function Gaussian_filtered = Gauss(image_x, sigma) % for single axis % http://en.wikipedia.org/wiki/Gaussian_filter Gaussian_filtered = exp(-image_x^2/(2*sigma^2)) / (sigma*sqrt(2*pi)); end对于2D高斯,
function h = Gaussian2D(hsize, sigma) n1 = hsize; n2 = hsize; for i = 1 : n2 for j = 1 : n1 % size is 10; % -5<center<5 area is covered. c = [j-(n1+1)/2 i-(n2+1)/2]'; % A product of both axes is 2D Gaussian filtering h(i,j) = Gauss(c(1), sigma)*Gauss(c(2), sigma); end end end最后一个是
function Filtered = GaussianFilter(ImageData, hsize, sigma) %Get the result of Gaussian filter_ = Gaussian2D(hsize, sigma); %check image [r, c] = size(ImageData); Filtered = zeros(r, c); for i=1:r for j=1:c for k=1:hsize for m=1:hsize Filtered = Filtered + ImageData(i,j).*filter_(k,m); end end end end end但处理后的图像与输入图像几乎相同。 我不知道最后一个函数GaussianFiltered()是有问题的...
谢谢。
I have tried to make a Gaussian filter in Matlab without using imfilter() and fspecial(). I have tried this but result is not like the one I have with imfilter and fspecial.
Here is my codes.
function Gaussian_filtered = Gauss(image_x, sigma) % for single axis % http://en.wikipedia.org/wiki/Gaussian_filter Gaussian_filtered = exp(-image_x^2/(2*sigma^2)) / (sigma*sqrt(2*pi)); endfor 2D Gaussian,
function h = Gaussian2D(hsize, sigma) n1 = hsize; n2 = hsize; for i = 1 : n2 for j = 1 : n1 % size is 10; % -5<center<5 area is covered. c = [j-(n1+1)/2 i-(n2+1)/2]'; % A product of both axes is 2D Gaussian filtering h(i,j) = Gauss(c(1), sigma)*Gauss(c(2), sigma); end end endand the final one is
function Filtered = GaussianFilter(ImageData, hsize, sigma) %Get the result of Gaussian filter_ = Gaussian2D(hsize, sigma); %check image [r, c] = size(ImageData); Filtered = zeros(r, c); for i=1:r for j=1:c for k=1:hsize for m=1:hsize Filtered = Filtered + ImageData(i,j).*filter_(k,m); end end end end endBut the processed image is almost same as the input image. I wonder the last function GaussianFiltered() is problematic...
Thanks.
最满意答案
这里有一个选择:
创建2D高斯:
function f=gaussian2d(N,sigma) % N is grid size, sigma speaks for itself [x y]=meshgrid(round(-N/2):round(N/2), round(-N/2):round(N/2)); f=exp(-x.^2/(2*sigma^2)-y.^2/(2*sigma^2)); f=f./sum(f(:));经过滤的图像,给定您的图像称为Im :
filtered_signal=conv2(Im,gaussian2d(N,sig),'same');这里有一些情节:
imagesc(gaussian2d(7,2.5)) Im=rand(100);subplot(1,2,1);imagesc(Im) subplot(1,2,2);imagesc(conv2(Im,gaussian2d(7,2.5),'same'));here's an alternative:
Create the 2D-Gaussian:
function f=gaussian2d(N,sigma) % N is grid size, sigma speaks for itself [x y]=meshgrid(round(-N/2):round(N/2), round(-N/2):round(N/2)); f=exp(-x.^2/(2*sigma^2)-y.^2/(2*sigma^2)); f=f./sum(f(:));Filtered image, given your image is called Im:
filtered_signal=conv2(Im,gaussian2d(N,sig),'same');Here's some plots:
imagesc(gaussian2d(7,2.5)) Im=rand(100);subplot(1,2,1);imagesc(Im) subplot(1,2,2);imagesc(conv2(Im,gaussian2d(7,2.5),'same'));更多推荐
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