Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from...
-
date post
20-Dec-2015 -
Category
Documents
-
view
215 -
download
0
Transcript of Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from...
![Page 1: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/1.jpg)
Image Enhancement in the Spatial Domain
(chapter 3)
Math 5467, Spring 2008
Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros
![Page 2: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/2.jpg)
Image Enhancement (Spatial)
• Image enhancement:
1. Improving the interpretability or perception of information in images for human viewers
2. Providing `better' input for other automated image processing techniques
• Spatial domain methods:
operate directly on pixels• Frequency domain methods:
operate on the Fourier transform of an image
![Page 3: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/3.jpg)
Point Processing• The simplest kind of range transformations
are these independent of position x,y:
g = T(f)
• This is called point processing.
• Important: every pixel for himself – spatial information completely lost!
![Page 4: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/4.jpg)
Obstacle with point processing• Assume that f is the clown image and T
is a random function and apply g = T(f):
• What we take from this?
1. May need spatial information
2. Need to restrict the class of transformation, e.g. assume monotonicity
![Page 5: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/5.jpg)
Basic Point Processing
![Page 6: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/6.jpg)
Negative
![Page 7: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/7.jpg)
Log Transform
![Page 8: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/8.jpg)
Power-law transformations
![Page 9: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/9.jpg)
Why power laws are popular?
• A cathode ray tube (CRT), for example, converts a video signal to light in a nonlinear way. The light intensity I is proportional to a power (γ) of the source voltage VS
• For a computer CRT, γ is about 2.2
• Viewing images properly on monitors requires γ-correction
![Page 10: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/10.jpg)
Gamma Correction
Gamma Measuring Applet: http://www.cs.cmu.edu/~efros/java/gamma/gamma.html
![Page 11: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/11.jpg)
Image Enhancement
![Page 12: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/12.jpg)
Contrast Streching
![Page 13: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/13.jpg)
Image Histograms
x-axis – values of intensitiesy-axis – their frequencies
![Page 14: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/14.jpg)
Back to previous example
The following two images
have the same histograms…
![Page 15: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/15.jpg)
Histogram Equalization (Idea)
• Idea: apply a monotone transform resulting in an approximately uniform histogram
![Page 16: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/16.jpg)
Histogram Equalization
![Page 17: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/17.jpg)
Cumulative Histograms
![Page 18: Image Enhancement in the Spatial Domain (chapter 3) Math 5467, Spring 2008 Most slides stolen from Gonzalez & Woods, Steve Seitz and Alexei Efros.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d435503460f94a1f892/html5/thumbnails/18.jpg)
How and why does it work ?
Why does it work: (to be explained in class)