Relational Algebra Prof. Yin-Fu Huang CSIE, NYUST Chapter 7.
Chap. 3: Image Enhancement in the Spatial Domain Spring 2006, Jen-Chang Liu CSIE, NCNU.
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Transcript of Chap. 3: Image Enhancement in the Spatial Domain Spring 2006, Jen-Chang Liu CSIE, NCNU.
Chap. 3: Image Enhancement in the Spatial Domain
Spring 2006, Jen-Chang LiuCSIE, NCNU
Announcement Where to find MATLAB ?
It’s not free… A book contains the student’s edition of
MATLAB NCNU CC support 50 on-line version in the
computer rooms (307, 308, 208, 413)
Image Enhancement Goal: process an image so that the
result is more suitable than the original image for a specific application
Visual interpretation Problem oriented
Image enhancement example
Two categories There is no general theory of image
enhancement Spatial domain
Direct manipulation of pixels Point processing Neighborhood processing
Frequency domain Modify the Fourier transform of an image
Outline: spatial domain operations
Background Gray level transformations Arithmetic/logic operations
Background
Spatial domain processing g(x,y)=T[ f(x,y) ] f(x,y): input image g(x,y): output image T: operator
Defined over some neighborhood of (x,y)
Tf(x,y) g(x,y)
Background (cont.)
* T operates over neighborhood of (x,y)
* T applies to each pixel in the input image
Point processing 1x1 neighborhood
Gray level transformation, or point processing
s = T(r)
contraststretching
thresholding
Neighborhood processing
A larger predefined neighborhood Ex. 3x3 neighborhood mask, filters, kernels, templates,
windows Mask processing or filtering
Some Basic Gray Level Transformations
Image negatives (complement) Log transformation Power-law transform Piece-wise linear transform Gray level slicing Bit plane slicing
Some gray level transformations
•Lookup table•Functional form
Image negatives Photographic negative 負片 Suitable for images with dominant black a
reas
Original mammogram( 乳房 X 光片 )
Log transformations s = c log(1+r)
Compress the dynamic range of images with large variation in pixel values
Example: Log transformations
log(fft2(I)) : log of Fourier transform
2d Fourier transform 頻譜圖
log
Power-law transformations
s=cr
>1 <1 : gamma
display, printers, scanners follow power-law
Gamma correction
Example: Gamma correction
CRT: intensity-to-voltage response follow a power-law. 1.8<<2.5
=2.5
=1/2.5
=2.5
原始影像 顯示器偏差
Power-law:
Expand dark gray levels
<1
=0.6
=0.3=0.4
Power-law: >1
Expand light gray levels
=4 =5
=3
Piece-wise linear transformations
Advantage: the piecewise function can be arbitrarily complex
control point
Contraststretching
How to automatically adjust the gray levels?
Gray-level slicing 切片 Highlighting a specific range of gray
levels
Bit-plane slicing* Highlight specific bits
bit-planes of an image(gray level 0~255)
Ex. 15010
10010100
Bit-plane slicing: example
7 6
5 4 3
2 1 0
For imagecompression
Arithmetic/logic operations
Logic operations Image subtraction Image averaging
Logic operations Logic operations: pixel-wise AND, OR,
NOT The pixel gray level values are taken as
string of binary numbers
Use the binary mask to take out the region of interest(ROI) from an image
Ex. 193 => 11000001
Logic operations: example
AND
OR
A B A and B
A or B
Image subtraction
Difference image g(x,y)=f(x,y)-h(x,y)
f:original(8 bits) h:4 sig. bits
difference image
scaling
Image subtraction: scaling the difference image
g(x,y)=f(x,y)-h(x,y) f and h are 8-bit => g(x,y) [-255, 255]
1. (1)+255 (2) divide by 2• The result won’t cover [0,255]
2. (1)-min(g) (2) *255/max(g)
Be careful of the dynamic range after the imageis processed.
Image subtraction example: mask mode radiography
Inject contrast medium into bloodstreamoriginal (head) difference image
注射碘液拍攝影像與原影像相減
Image averaging Noisy image g(x,y)=f(x,y)+η(x,y)
Suppose η(x,y) is uncorrelated and has zero mean
original noise
Clear image Noisy image
Image averaging: noise reduction
),(),( yxfyxgE
2),(
2),(
1yxyxg K
期望值接近原圖
2K
Averaging over K noisy images gi(x,y)
K
ii yxg
Kyxg
1
),(1
),(
original Gaussiannoise
averagingK=8
averagingK=16
averagingK=64
averagingK=128