数字图像处理课件lecture2-2013
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Mapping Table
gin 0 1 2 3 4
gout 4
6
8
2
1
15
Various Mapping Functions
gout
1 S 1 (m / r ) E
gout
gout
gin
gout gout
gin
gout
gin
wk.baidu.com
gin
gin
gin
16
•Contrast stretching
6
Example5
(a)
(b)
(c)
(d)
(a) Original sensed fingerprint; (b) Image enhanced of ridges; (c) Thinning of ridges; (d) Identification of special features called minutia, which can be used for matching to millions of 7 fingerprint representations in a database.
Image enhancement operators improve the detectability of important image details or objects by man or machine. Example operations include noise reduction (smoothing), contrast stretching, and sharpening (edge enhancement).
Definition - A point operator applied to an image is an operator in which the output pixel is determined only by the input pixel, Out[x, y] = f (In[x, y]);
•Remapping the gray values is often called stretching 伸缩 • A contrast stretching 对比度伸缩 operator is a point operator that uses a piecewise smooth function f (In[x, y]) of the input gray level to enhance important details of the image.
31
Using (a) transformation
Intensity Level Slicing
(a) Visual and infrared images
(b) Segment images
33
Level slicing of intensity window[175,250]
•Bit-plane Slicing
Noise Smoothing Median Filtering Sharping Masking Zooming
•Histogram Transforms
2 (3) Image Enhancement in the Frequency Domain Operation
What Needs Enhancement?
• Frequency Domain
9
Points Operation
What is the Gray level transformations?
Two ways to enhance images: Changing the intensity values of pixels Transforming the pixels via a single function that maps an input gray value into a new output value
10
Points Operation Linear transformation
Dout af ( Din ) b
11
a=0, b=1 a<0: inverse |a|>1: Increasing the contrast |a|<1: reduce the contrast b>0: increasing the brightness b<0: reduce the brightness
thresholding
28
Contrast Stretching
thresholding
29
•Intensity Level Slicing 灰度级切片
Without background:
L a u b v 0 otherwise
L a u b v u otherwise
12
Points Operation
Nonlinear transformations
f ( I ( x, y)) I ( x, y) C * I ( x, y) *( I ( x, y)m I ( x, y))
•Brightness adjustment
13
14
Points Operation
Output image
0
Input image
gin
255
Output image
22
Contrast Stretching
(a) Original
(b) Enhanced
23
Contrast Stretching
(a) Original
(b) Enhanced
24
Contrast Stretching
3
Scratches
Example 1: Scratches from original photo of San Juan are removed
Example 2: Intensity of photo of Alaskan Pipeline rescaled to show much better detail
Example 3: Image of airplane part has edges enhanced to support automatic recognition and measurement
Example 4: Image of face has enhanced to support automatic recognition
clear all I = imread('cameraman.tif'); [h w] = size(I); subplot(3,3,1); imshow(I);title('原始图像'); for k=1:8 for i=1:h for j=1:w b1(i,j) = bitget(I(i,j),k); end end subplot(3,3,k+1); imshow(b1,[]); ind = num2str(k);imti = ['第',ind,'个位平面']; title(imti); end
• Can define other similar operations.
u 0 u a v u a va a u b u b v b u L b
26
27
Contrast Stretching
Where u is the gray-level values of input image 0-255, v is the transformed gray-level values of output image 0-255, L=255
21
Mapping function
255 gout f ( x)
8
Image Enhancement - Operations
• Spatial Domain g(x,y)=T(f(x,y)) (1) Points operations: Gray level transformations
(2)Spatial (mask) operation: Spatial filters (3)Histogram Transforms
With background:
30
Intensity Level Slicing
Fully illuminates pixels lying in the interval [a,b] and removes the background. • Segmentation of certain gray level region • e.g. Image from remote sensing.
20
Contrast Stretching
•Typical contrast stretching transformation:
u 0 u a v u a va a u b u b v b u L b
255 255 142
48
218
255
255
1
0.5
0.75
1 19
Contrast Stretching
• Improve contrast due to poor /nonuniform lighting conditions or nonlinearity or small dynamic range of image sensor.
Before launching into the methods of this chapter, it is useful to review some of the problems that need them. Two general categories of problems follow. An image needs improvement Sharpening of image features ( such as edges or boundaries) to make a graphic display more useful for analysis. Low-level features must be detected
1
Contents
(1) What Needs Enhancement?
(2) Image Enhancement in the Spatial Domain •Point Operation
Linear Point Operation Nonlinear Point Operation
•Spatial (mask) Operation
• Example:
(a) Original
(b) Enhanced
25
Contrast Stretching
• Thresholding: when a=b=t (threshold)
– Output becomes binary. – Useful for binary or other images that have bimodal distance of gray levels.
Chapter 3: Filtering & Enhancing Images
This chapter is about image processing, since the methods take an input image and create another image as output. Other appropriate terms often used are filtering, enhancement, or conditioning. The major motion is that the image contains some signal or structure, which we want to extract, along with uninteresting or unwanted variation, which we want to suppress. If decisions are made about the image, they are made at the level of a single pixel or its local neighborhood.
gin 0 1 2 3 4
gout 4
6
8
2
1
15
Various Mapping Functions
gout
1 S 1 (m / r ) E
gout
gout
gin
gout gout
gin
gout
gin
wk.baidu.com
gin
gin
gin
16
•Contrast stretching
6
Example5
(a)
(b)
(c)
(d)
(a) Original sensed fingerprint; (b) Image enhanced of ridges; (c) Thinning of ridges; (d) Identification of special features called minutia, which can be used for matching to millions of 7 fingerprint representations in a database.
Image enhancement operators improve the detectability of important image details or objects by man or machine. Example operations include noise reduction (smoothing), contrast stretching, and sharpening (edge enhancement).
Definition - A point operator applied to an image is an operator in which the output pixel is determined only by the input pixel, Out[x, y] = f (In[x, y]);
•Remapping the gray values is often called stretching 伸缩 • A contrast stretching 对比度伸缩 operator is a point operator that uses a piecewise smooth function f (In[x, y]) of the input gray level to enhance important details of the image.
31
Using (a) transformation
Intensity Level Slicing
(a) Visual and infrared images
(b) Segment images
33
Level slicing of intensity window[175,250]
•Bit-plane Slicing
Noise Smoothing Median Filtering Sharping Masking Zooming
•Histogram Transforms
2 (3) Image Enhancement in the Frequency Domain Operation
What Needs Enhancement?
• Frequency Domain
9
Points Operation
What is the Gray level transformations?
Two ways to enhance images: Changing the intensity values of pixels Transforming the pixels via a single function that maps an input gray value into a new output value
10
Points Operation Linear transformation
Dout af ( Din ) b
11
a=0, b=1 a<0: inverse |a|>1: Increasing the contrast |a|<1: reduce the contrast b>0: increasing the brightness b<0: reduce the brightness
thresholding
28
Contrast Stretching
thresholding
29
•Intensity Level Slicing 灰度级切片
Without background:
L a u b v 0 otherwise
L a u b v u otherwise
12
Points Operation
Nonlinear transformations
f ( I ( x, y)) I ( x, y) C * I ( x, y) *( I ( x, y)m I ( x, y))
•Brightness adjustment
13
14
Points Operation
Output image
0
Input image
gin
255
Output image
22
Contrast Stretching
(a) Original
(b) Enhanced
23
Contrast Stretching
(a) Original
(b) Enhanced
24
Contrast Stretching
3
Scratches
Example 1: Scratches from original photo of San Juan are removed
Example 2: Intensity of photo of Alaskan Pipeline rescaled to show much better detail
Example 3: Image of airplane part has edges enhanced to support automatic recognition and measurement
Example 4: Image of face has enhanced to support automatic recognition
clear all I = imread('cameraman.tif'); [h w] = size(I); subplot(3,3,1); imshow(I);title('原始图像'); for k=1:8 for i=1:h for j=1:w b1(i,j) = bitget(I(i,j),k); end end subplot(3,3,k+1); imshow(b1,[]); ind = num2str(k);imti = ['第',ind,'个位平面']; title(imti); end
• Can define other similar operations.
u 0 u a v u a va a u b u b v b u L b
26
27
Contrast Stretching
Where u is the gray-level values of input image 0-255, v is the transformed gray-level values of output image 0-255, L=255
21
Mapping function
255 gout f ( x)
8
Image Enhancement - Operations
• Spatial Domain g(x,y)=T(f(x,y)) (1) Points operations: Gray level transformations
(2)Spatial (mask) operation: Spatial filters (3)Histogram Transforms
With background:
30
Intensity Level Slicing
Fully illuminates pixels lying in the interval [a,b] and removes the background. • Segmentation of certain gray level region • e.g. Image from remote sensing.
20
Contrast Stretching
•Typical contrast stretching transformation:
u 0 u a v u a va a u b u b v b u L b
255 255 142
48
218
255
255
1
0.5
0.75
1 19
Contrast Stretching
• Improve contrast due to poor /nonuniform lighting conditions or nonlinearity or small dynamic range of image sensor.
Before launching into the methods of this chapter, it is useful to review some of the problems that need them. Two general categories of problems follow. An image needs improvement Sharpening of image features ( such as edges or boundaries) to make a graphic display more useful for analysis. Low-level features must be detected
1
Contents
(1) What Needs Enhancement?
(2) Image Enhancement in the Spatial Domain •Point Operation
Linear Point Operation Nonlinear Point Operation
•Spatial (mask) Operation
• Example:
(a) Original
(b) Enhanced
25
Contrast Stretching
• Thresholding: when a=b=t (threshold)
– Output becomes binary. – Useful for binary or other images that have bimodal distance of gray levels.
Chapter 3: Filtering & Enhancing Images
This chapter is about image processing, since the methods take an input image and create another image as output. Other appropriate terms often used are filtering, enhancement, or conditioning. The major motion is that the image contains some signal or structure, which we want to extract, along with uninteresting or unwanted variation, which we want to suppress. If decisions are made about the image, they are made at the level of a single pixel or its local neighborhood.