Matlab learning 8- linear and nonlinear sharpening filtering and nonlinear smoothing filtering of image processing

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Preface

Spatial filtering enhancement

• Convolution principle
  • Multidimensional continuous convolution
• Linear smoothing filtering
  • Domain average method 、 Selective averaging 、Wiener wave filtering
• Nonlinear smoothing filtering
  • median filtering
  • Ordinal statistical filtering
• Linear sharpening filter
  • Laplacian operator
• Nonlinear sharpening filter
  • Prewitt operator
  • Sobel operator
  • Log operator

Matlab Study 8- Linear and nonlinear sharpening filtering for image processing
Ordinal statistical filtering 、Laplacian operator 、 Gradient method

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One 、 Ordinal statistical filtering

effect

Code

% Order statistical filter 
img=imread("img/eight.tif");
subplot(2,3,1),imshow(img),xlabel(" original image ");

img1=imnoise(img,'salt & pepper',0.1);
subplot(2,3,2),imshow(img1),xlabel(" Salt and pepper noise image ");

mask=[  0 1 0
        1 1 1
        0 1 0];
subplot(2,3,3),imshow(mask),xlabel(" Filter template ");

K1=ordfilt2(img1,3,mask);
subplot(2,3,4),imshow(uint8(K1)),xlabel(" Median filter image ");

K2=ordfilt2(img1,1,mask);
subplot(2,3,5),imshow(uint8(K2)),xlabel(" Minimum filter image ");

K3=ordfilt2(img1,5,mask);
subplot(2,3,6),imshow(uint8(K3)),xlabel(" Maximum filtered image ");

Two 、 Laplace operator

effect

Code

% Examples of Laplacian operators 
img=imread("img/eight.tif");
subplot(3,3,1),imshow(img),xlabel(" original image ");
mask1=fspecial('laplacian',0);
L1=filter2(mask1,img);
subplot(3,3,2),imshow(uint8(L1)),text(-14,285,' Horizontal and vertical template filter image with negative center coefficient ');
subplot(3,3,3),imshow(uint8(double(img)-L1)),xlabel(' Horizontal and vertical template subtraction superimposed image with negative center coefficient ');

mask2=-mask1;
L2=filter2(mask2,img);
subplot(3,3,4),imshow(uint8(L2)),text(-14,285,' Horizontal and vertical template filtering image with positive center coefficient ');
subplot(3,3,5),imshow(uint8(double(img)+L2)),xlabel(' The horizontal and vertical templates with positive center coefficient are added to the superimposed image ');

mask3=[ 1  1  1
        1 -8  1
        1  1  1];
L3=filter2(mask3,img);
subplot(3,3,6),imshow(uint8(L3)),text(-28,285,' Horizontal vertical diagonal template filter image with negative center coefficient ');
subplot(3,3,7),imshow(uint8(double(img)-L3)),xlabel(' Horizontal vertical diagonal template subtraction superimposed image with negative center coefficient ');

mask4=-mask3;
L4=filter2(mask4,img);
subplot(3,3,8),imshow(uint8(L4)),text(-28,285,' Horizontal vertical diagonal template filter image with positive center coefficient ');
subplot(3,3,9),imshow(uint8(double(img)+L4)),xlabel(' The horizontal vertical diagonal template with positive center coefficient is added to the superimposed image ');

3、 ... and 、 Gradient filtering

effect

Code

% Gradient filtering 
img=imread("img/img.jpg");
subplot(2,3,1),imshow(img),xlabel(" original image ");

img=double(img);
[Gx,Gy]=gradient(img);
G=sqrt(Gx.*Gx+Gy.*Gy);

img1=G;
subplot(2,3,2),imshow(uint8(img1)),xlabel(" Gradient filter image ");


img2=img;
K=find(G>=7);
img2(K)=G(K);
subplot(2,3,3),imshow(uint8(img2)),xlabel(" The filtered image of the first case ");

img3=img;
K=find(G>=7);
img3(K)=255;
subplot(2,3,4),imshow(uint8(img3)),xlabel(" The second case is the filtered image ");

img4=G;
K=find(G<=7);
img4(K)=255;
subplot(2,3,5),imshow(uint8(img4)),xlabel(" The third case is the filtered image ");

img5=img;
K=find(G<=7);
img5(K)=0;
Q=find(G>=7);
img5(Q)=255;
subplot(2,3,6),imshow(uint8(img5)),xlabel(" The fourth case is the filtered image ");

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Click to get the source code
https://gitee.com/CYFreud/matlab/tree/master/image_processing/demo8_220425