Digital Image Processing Using Matlab: Basic transformations, filters, operators
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Transcript of Digital Image Processing Using Matlab: Basic transformations, filters, operators
NATIONAL CHENG KUNG UNIVERSITY
Inst. of Manufacturing Information & Systems
DIGITAL IMAGE PROCESSING AND SOFTWARE
IMPLEMENTATION
HOMEWORK 1
Professor name: Chen, Shang-Liang
Student name: Nguyen Van Thanh
Student ID: P96007019
Class: P9-009 Image Processing and Software Implementation
Time: [4] 2 4
1
Table of Contents PROBLEM ................................................................................................................................................................. 2
SOLUTION ................................................................................................................................................................ 3
3.2.1 Negative transformation ............................................................................................................................ 3
3.2.2 Log transformation ..................................................................................................................................... 3
3.2.3 Power-law transformation ......................................................................................................................... 4
3.2.4 Piecewise-linear transformation ................................................................................................................ 7
3.3.1 Histogram equalization............................................................................................................................. 10
3.4.2 Subtraction ............................................................................................................................................... 12
3.6.1 Smoothing Linear Filters ........................................................................................................................... 14
3.6.2 Order-Statistics Filters .............................................................................................................................. 16
3.7.2 The Laplacian ............................................................................................................................................ 17
3.7.3 The Gradient ............................................................................................................................................. 19
2
PROBLEM
影像處理與軟體實現[HW1] 課程碼:P953300 授課教授:陳響亮 教授 助教:陳怡瑄 日期:2011/03/10
題目:請以C# 撰寫一程式,可讀入一影像檔,並可執行以下之影像
空間強化功能。
a. 每一程式需設計一適當之人機操作介面。
b. 每一功能請以不同方法分開撰寫,各項參數需讓使用者自行輸入。
c. 以C# 撰寫時,可直接呼叫Matlab 現有函式,但呼叫多寡,將列為評分考量。
(呼叫越少,分數越高)
一、 基本灰階轉換
1. 影像負片轉換
2. Log轉換
3. 乘冪律轉換
4. 逐段線性函數轉換
二、 直方圖處理
1. 直方圖等化處理
2. 直方圖匹配處理
三、 使用算術/邏輯運算做增強
1. 影像相減增強
2. 影像平均增強
四、 平滑空間濾波器
1. 平滑線性濾波器
2. 排序統計濾波器
五、 銳化空間濾波器
1. 拉普拉斯銳化空間濾波器
2. 梯度銳化空間濾波器
3
SOLUTION
Using Matlab for solving the problem
3.2.1 Negative transformation
Given an image (input image) with gray level in the interval [0, L-1], the negative of that
image is obtained by using the expression: s = (L – 1) – r,
Where r is the gray level of the input image, and s is the gray level of the output.
In Matlab, we use the commands,
>> f=imread('Fig3.04(a).jpg'); g = imcomplement(f); imshow(f), figure, imshow(g)
In/output image Out/in image
3.2.2 Log transformation
The Logarithm transformations are implemented using the expression:
s = c*log (1+r).
In this case, c = 1. The commands,
>> f=imread('Fig3.05(a).jpg'); g=im2uint8 (mat2gray (log (1+double (f)))); imshow(f), figure, imshow(g)
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In/output image Out/in image
3.2.3 Power-law transformation
Power-law transformations have the basic form,
s = c*r. ^, where c and are positive constants.
The commands,
>> f = imread ('Fig3.08(a).jpg');
f = im2double (f);
[m n]=size (f);
c = 1;
gama = input('gama value = ');
for i=1:m
for j=1:n
g(i,j)=c*(f(i,j)^gama);
end
end;
imshow(f),figure, imshow(g);
With = 0.6, 0.4 and 0.3 respectively, we can get three images respectively, as shown in the
following figure,
5
a b
c d
(a) The original image. (b) – (d) result of applying the power -
law transformation with = 0.6, 0.4 and 0.3 respectively
6
a b
c d
(a) The original image. (b) – (d) result of applying the power -
law transformation with = 3, 4 and 5 respectively
7
3.2.4 Piecewise-linear transformation
Contrast stretching
The commands, % function contrast stretching;
>> r1 = 100; s1 = 40; r2 = 141; s2 = 216; a = (s1/r1); b = ((s2-s1)/ (r2-r1)); c = ((255-s2)/ (255-r2)); k = 0:r1; y1 = a*k; plot (k,y1); hold on; k = r1: r2; y2 = b*(k - r1) + a*r1; plot (k,y2); k = r2+1:255; y3 = c*(k-r2) + b*(r2-r1)+a*r1; plot (k,y3); xlim([0 255]); ylim([0 255]); xlabel('input gray level, r'); ylabel('outphut gray level, s'); title('Form of transformation'); hold on; figure; f = imread('Fig3.10(b).jpg'); [m, n] = size (f); for i = 1:m for j = 1:n if((f(i,j)>=0) & (f(i,j)<=r1)) g(i,j) = a*f(i,j); else if((f(i,j)>r1) & (f(i,j)<=r2)) g(i,j) = ((b*(f(i,j)-r1)+(a*r1))); else if((f(i,j)>r2) & (f(i,j)<=255)) g(i,j) = ((c*(f(i,j)-r2)+(b*(r2-r1)+(a*r1)))); end end end end end
imshow(f), figure, imshow(g);
% function thresholding
>> f = imread('Fig3.10(b).jpg');
[m, n] = size(f);
for i = 1:m
for j = 1:n
if((f(i,j)>=0) & (f(i,j)<128))
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g(i,j) = 0;
else
g(i,j) = 255;
end
end
end
imshow(f), figure, imshow(g);
(a) Form of contrast stretching transformation function.
(b) A low-contrast image. (c) Result of contrast stretching. (d)
Result of thresholding
a b
c d
9
(a) An 8-bit image. (b) – (f) The 8 bit plane
a b c
d e f
10
3.3.1 Histogram equalization
The transformation function of histogram equalization is
( ) ∑ ( )
∑
k = 0, 1, …, L – 1.
% Histogram;
f1 = imread('Fig3.15(a)1top.jpg');
f2 = imread('Fig3.15(a)2.jpg');
f3 = imread('Fig3.15(a)3.jpg');
f4 = imread('Fig3.15(a)4.jpg');
f = input('image: ');
imhist(f), figure;
g = histeq(f, 256);
imshow(g), figure, imhist(g);
a b c
Fig. 3.17 Transformation functions (1) through (4) were obtained from the
images in Fig. 3.17 (a), using histogram equalization
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a b
Fig. 3.15 Four
basic image
types: dark,
light, low
contrast, high
contrast, and
their
corresponding
histograms
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a b c
Fig. 3.17 (a) Image from Fig. 3.15. (b) Results of histogram equalization. (c)
Corresponding histograms.
13
3.4.2 Subtraction
The difference between tow images f (x, y) and h (x, y), expressed as
g (x, y) = f (x, y) – h (x, y),
The commands,
f1 = imread('Fig3.28.a.jpg'); f2 = imread('Fig3.28.b.jpg'); f3 = imsubtract(f1,f2); f4 = histeq(f3,256); imshow(f3), figure, imshow(f4);
a b
c d
Fig. 3.17 (a) The first image. (b) The second image. (c) Difference between (a) and
(b). (d) Histogram – equalized difference image.
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3.6.1 Smoothing Linear Filters
The commands,
f = imread('Fig3.35(a).jpg'); w3 = 1/ (3. ^2)*ones (3); g3 = imfilter (f, w3, 'conv', 'replicate', 'same'); w5 = 1/ (5. ^2)*ones (5); g5 = imfilter (f, w5, 'conv', 'replicate', 'same'); w9 = 1/ (9. ^2)*ones (9); g9 = imfilter (f, w9, 'conv', 'replicate', 'same'); w15 = 1/ (15. ^2)*ones (15); g15 = imfilter (f, w15, 'conv', 'replicate', 'same'); w35 = 1/ (35. ^2)*ones (35); g35 = imfilter(f, w35, 'conv', 'replicate', 'same'); imshow (g3), figure, imshow (g5), figure, imshow (g9), figure, imshow
(g15), figure, imshow (g35), figure; h = imread ('Fig3.36(a).jpg'); h15 = imfilter (h, w15, 'conv', 'replicate', 'same'); [m, n] = size (h15); for i = 1:m for j = 1:n if ((h15 (i,j)>=0) & (h15 (i,j)<128)) g (i,j) = 0; else g(i,j) = 255; end end end imshow(h15), figure, imshow(g);
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Fig. 3.35 (a) Original image, of size 500 x 500 pixels. (b) – (f) Result of
smoothing with square averaging filter masks of size n = 3, 5, 9, 15,
and 35 respectively.
a b
c d
e f
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3.6.2 Order-Statistics Filters
The commands,
>> f = imread('Fig3.37(a).jpg'); w3 = 1/(3.^2)*ones(3); g3 = imfilter(f, w3, 'conv', 'replicate', 'same'); g = medfilt2(g3); imshow(g3), figure, imshow(g);
a b c Fig. 3.36 (a) Original image. (b) Image processed by a 15 x 15 averaging mask.
(c) Result of thresholding (b)
Fig. 3.37 (a) X – ray image of circuit board corrupted by salt – and –
pepper noise. (b) Noise reduction with a 3 x 3 averaging mask. (c)
Noise reduction with a 3 x 3 median filter
a b c
17
3.7.2 The Laplacian
The Laplacian for image enhancement is as follows:
( )
{
( ) ( )
( ) ( )
( )
The commands,
% Laplacian function
f1 = imread('Fig3.40(a).jpg'); w4 = fspecial('laplacian', 0); g1 = imfilter(f1, w4, 'replicate'); imshow(g1, [ ]), figure; f2 = im2double(f1); g2 = imfilter(f2, w4, 'replicate'); imshow(g2, [ ]), figure; g3 = imsubtract(f2,g2); imshow(g3)
Fig. 3.40 (a) Image of
the North Pole
of the moon.
(b) Laplacian
image scaled
for display
purposes. (d)
Image
enhanced by
Eq. (3.7 – 5)
a b
c d
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% Laplacian simplication f1 = imread ('Fig3.41(c).jpg'); w5 = [0 -1 0; -1 5 -1; 0 -1 0]; g1 = imfilter (f1, w5, 'replicate'); imshow (g1), figure; w9 = [-1 -1 -1; -1 9 -1; -1 -1 -1]; g2 = imfilter (f1, w9, 'replicate'); imshow (g2);
0 -1 0
-1 5 -1
0 -1 0
-1 -1 -1
-1 9 -1
-1 -1 -1
a b c
d e
Fig. 3.37 (a) Composite Laplacian mask. (b) A second composite
mask. (c) Scanning electron microscope image. (d) and (e)
Result of filtering with the masks in (a) and (b) respectively.
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3.7.3 The Gradient
The commands,
>> f1 = imread('Fig3.45(a).jpg');
w = fspecial('sobel'); g1 = imfilter(f1, w, 'replicate'); imshow(g1);
a b Fig. 3.45 (a) Optical image of contact lens (note defects on the
boundary at 4 and 5 o’clock). (b) Sobel gradient