Abstract
Different aspects of digital image processing have been considered, explained, and illustrated experimentally in this chapter. For the readers that are beginners in the field of image processing, the basic mathematical concepts behind the common arithmetical and geometrical image operations are explained in detail, as well as different image-filtering algorithms (arithmetic and geometric mean filter, median and alpha-trimmed median filter, frequency domain filtering, etc). The second part of this chapter discusses the image compression algorithms including DCT-based JPEG, progressive and hierarchical JPEG, wavelet-based JPEG2000 algorithm, and Fractal image compression. The quantization and coding procedures for JPEG and JPEG2000 algorithms have been explained in detail. Finally, some interesting edge detection, image segmentation, and texture characterization algorithms have been presented as well.
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Appendix – Matlab Codes for Some of the Considered Image Transforms
Appendix – Matlab Codes for Some of the Considered Image Transforms
IMAGE CLIPPING
I=imread(lena512.bmp);
I=I(1:2:512,1:2:512);
I=double(I);
for i=1:256
for j=1:256
if I(i,j)<100
I(i,j)=100;
elseif I(i,j)>156
I(i,j)=156;
end
end
end
I=uint8(I);
imshow(I)
TRANSFORMING IMAGE LENA TO IMAGE BABOON
Ia=imread( lena512.bmp );
Ia=Ia(1:2:512,1:2:512);
Ia=double(Ia);
Ib=imread(baboon.jpg);
Ib=rgb2gray(Ib);
Ib=double(Ib);
for i=1:10
Ic=(1-i/10)*Ia+(i/10)*Ib;
imshow(uint8(Ic))
pause(0.5)
end
GEOMETRIC MEAN FILTER
clear all
I=imread( board.tif );
I=imnoise(I, gaussian,0,0.025);
I=double(I);
[m,n]=size(I);
Im=zeros(size(I));
for i=1:m
for j=1:n
a=I(max(i,i-1):min(m,i+1),max(j,j-1):min(n,j+1));
Im(i,j)=geomean(a(:));
end
end
figure(1), imshow(uint8(I))
figure(2), imshow(uint8(Im))
CONSECUTIVE IMAGE ROTATIONS (Image is rotated in steps of 5° up to 90°)
I=imread( lena512.bmp );
I=I(1:2:512,1:2:512);
for k=5:5:90
I1=imrotate(I,k, nearest );
imshow(I1)
pause(1)
end
SOBEL EDGE DETECTOR version1
I=imread( cameraman.tif );
subplot(221),imshow(I)
edge_h=edge(I, sobel, horizontal );
subplot(222),imshow(edge_h)
edge_v=edge(I, sobel, vertical);
subplot(223),imshow(edge_v)
edge_b=edge(I, sobel, both );
subplot(224),imshow(edge_b)
SOBEL EDGE DETECTOR version2
WITH AN ARBITRARY GLOBAL THRESHOLD
clear all
I=imread( lena512.bmp );
I=I(1:2:512,1:2:512);
[m,n]=size(I);
I=double(I);
H=[1 2 1; 0 0 0; -1 -2 -1];
V=[1 0 -1; 2 0 -2; 1 0 -1];
Edge_H=zeros(m,n);
Edge_V=zeros(m,n);
Edges=zeros(m,n);
thr=200;
for i=2:m-1
for j=2:n-1
Lv=sum(sum(I(i-1:i+1,j-1:j+1).*V));
Lh=sum(sum(I(i-1:i+1,j-1:j+1).*H));
L=sqrt(Lv^2+Lh^2);
if Lv>thr
Edge_V(i,j)=255;
end
if Lh>thr
Edge_H(i,j)=255;
end
if L>thr
Edges(i,j)=255;
end
end
end
figure, imshow(uint8(Edge_H))
figure, imshow(uint8(Edge_V))
figure, imshow(uint8(Edges))
WAVELET IMAGE DECOMPOSITION
I=imread( lena512.bmp );
I=double(I);
n=max(max(I));
%First level decomposition
[S1,H1,V1,D1]=dwt2(I, haar );
S1=wcodemat(S1,n);
H1=wcodemat(H1,n);
V1=wcodemat(V1,n);
D1=wcodemat(D1,n);
dec2d_1 = [S1 H1; V1 D1];
%Next level decomposition
I=S1;
[S2,H2,V2,D2]=dwt2(I, haar );
S2=wcodemat(S2,n);
H2=wcodemat(H2,n);
V2=wcodemat(V2,n);
D2=wcodemat(D2,n);
dec2d_2 = [S2 H2; V2 D2];
dec2d_1 = [dec2d_2 H1; V1 D1];
imshow(uint8(dec2d_1))
JPEG IMAGE QUANTIZATION
I=imread( lena.jpg );
I=rgb2gray(I);
I=double(I(1:2:512,1:2:512));
Q50=[16 11 10 16 24 40 51 61;
12 12 14 19 26 58 60 55;
14 13 16 24 40 57 69 56;
14 17 22 29 51 87 80 62;
18 22 37 56 68 109 103 77;
24 35 55 64 81 104 113 92;
49 64 78 87 103 121 120 101;
72 92 95 98 112 100 103 99];
QF=70;
q=2-0.02*QF; %q=50/QF;
Q=round(Q50.*q);
I1=zeros(256,256);
for i=1:8:256-7
for j=1:8:256-7
A=I(i:i+7,j:j+7);
dct_block=dct2(A);
dct_Q=round(dct_block./Q).*Q;
I1(i:i+7,j:j+7)=idct2(dct_Q);
end
end
figure(1), imshow(uint8(I))
figure (2), imshow (uint8(I1))
Table 4.4
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Stanković, S., Orović, I., Sejdić, E. (2012). Digital Image. In: Multimedia Signals and Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-4208-0_4
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DOI: https://doi.org/10.1007/978-1-4614-4208-0_4
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