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Chapter 2 Digital Image fundamentals
1. Elements of visual perception
2. Light and the electromagnetic spectrum
3. Image sensing and acquisition
4. Image sampling and quantization
5. Some basic relationships between pixels
6. Linear and Nonlinear Operations
Ā© 2002 R. C. Gonzalez & R. E. Woods
1. Elements of visual perception
1. Structure of the Human Eye
ļ® Light Reception
ļ§ Cones
ļ§ 6-7millions
ļ§ sensitive to Color
ļ§ Photopic or Bright-light vision
ļ§ Rods
ļ§ 75-150millions
ļ§ Scotopic or Dim-light vision
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.1 Elements of visual perception
ļ® 2.1.2 Image formation in the Eye
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.1 Elements of visual perception
ļ® 2.1.3 Brightness adaptation and discrimination
- (^) Subjective Brightness is a logarithm function
- (^) The total range of distinct intensity levels it can
discriminate simultaneously is rather small
Ā© milliLambert: 2002 R. C. Gonzalez äŗ®åŗ¦å®ä½ & R. E. Woods
2.1 Elements of visual perception
ļ® A classic experiment for brightness discrimination
- (^) Weber Ratio : ļ Ic / I
- (^) a small value means Good brightness discrimination
- (^) a large value means Poor brightness discrimination
- (^) the typical observer can discern 1~2 dozen different
intensity changes
Scotopic
Photopic
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.1 Elements of visual perception
ļ® Two examples
ļ§ to demonstrate the perceived brightness is not a simple
function of intensity
ļ§ Mach Bands - 1865, Ernst Mach
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.1 Elements of visual perception
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.1 Elements of visual perception
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.1 Elements of visual perception
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.1 Elements of visual perception
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.1 Elements of visual perception
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
Light and the Electromagnetic Spectrum
ļ® Electromagnetic Spectrum
Ā© E. 2002 R. Woods C. Gonzalez & R.
Light and the Electromagnetic Spectrum
ļ® Wavelength ļ¬
ļ® To calculate the wave length and frequency of
color
ļ® Electromagnetic spectrum given with the
expression
h ļ½ 6.626068ļ“ 10 ļ^34 Js 1 eV ļ½ 1.6 ļ“ 10 ļ^19 J
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
Light and the Electromagnetic Spectrum
ļ® Visible Band
ļ®430um~790nm
ļ®Illuminant
ļ®reflected light from objects
ļ®Achromatic or Monochromatic (Gray Level)
ļ®Chromatic - Color
ļ®Light Source
ļ® (^) Radiance - measured in Watts ļ® (^) Luminance - measured in lumens ļ® (^) Brightness - subjective descriptor Ā© 2002 R. C. Gonzalez & R. E. Woods
2.3 Image Sensing and Acquisition
ļ® 2.3.1 Image acquisition using a single sensor
ļ§ Photodiode
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.3 Image Sensing and Acquisition
ļ® 2.3.2 Image acquisition using sensor strips
Scanning Direction
Flatbed Scanner
CAT
MRI
PET
Ā© 2002 R. C. Gonzalez & R. E. Woods 13
2.3 Image Sensing and Acquisition
ļ® 2.3.3 Image acquisition using sensor arrays
ļ§ Charged Couple Diode (CCD)
Sun
Lamp
Flash
Iris
Len
s
Digi
tal
Cam
era
CCD
CMOS
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.4 Image sampling and quantization
ļ® 2.4.1 Basic concepts in sampling and quantization
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.4 Image sampling and quantization
Sampling Quantization
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.4 Image sampling and quantization
ļ® 2.4.2 Representing digital images
Compact Matrix Form(2.4-1)
Traditional Matrix Form(2.4-2)
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.4 Image sampling and quantization
ļ® Storage space for digital images
ļ®For Gray-Level L=2k, k bits required for a pixel
ļ®For an M ļ“ N image, b= M ļ“ N ļ“ k bits required for an image
ļ®For an N ļ“ N image, b= N^2 k bits required for an image
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.4 Image sampling and quantization
Gray-Level Resolution
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.4 Image sampling and quantization
Gray-Level Resolution
Ā© 2002 R. C. Gonzalez & R. E. Woods
2.4 Image sampling and quantization
Spatial and Gray-Level Resolution
Ā© 2002 R. C. Gonzalez & R. E. Woods
Digital Image Processing, 2nd ed. www.imageprocessingbook.com
2.4 Image sampling and quantization
Spatial and Gray-Level Resolution
similar quality
Ā© 2002 R. C. Gonzalez & R. E. Woods
