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Chapter 2: Digital Image Fundamentals
Two types of receptors on retina: Cones & Rods Cones: sensitive to bright light; ~ photopic vision; can resolve fine details; sensitive to color; 6 to 7 million cones Rods: sensitive to dim light; ~ scotopic vision; give overall picture; not involved in color vision. 75 to 150 million rods.
Structure of the human eye
Chapter 2: Digital Image Fundamentals
Cones: most dense in the center of retina (fovea). Rods: much more than cones, and distributed more consistently on retina. Blind spot: no cones, no rods.
Distribution of cones and rods
Chapter 2: Digital Image Fundamentals
The thickness of lens is variable Lens becomes thicker when focused on a nearby object. Image of the object is primarily displayed in the area of the fovea.
The retinal image of an object
Chapter 2: Digital Image Fundamentals
Subjective brightness is a logarithmic function of light intensity; HVS can adapt to a huge range of light intensity levels (order of 1010 ); But the range of simultaneous operation by HVS is small.
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Adaptation level of HVS
Chapter 2: Digital Image Fundamentals
The eye’s ability of identifying the bright change depends on the adaptation level; ΔI/I (Weber ratio) is used to measure the eye’s discrimination ability; A small value of ΔI/I means “good” brightness discrimination (a small percentage change is discriminable);
Discrimination of brightness change
Chapter 2: Digital Image Fundamentals Sample curve
Poor discrimination ability at low level of intensities!
Chapter 2: Digital Image Fundamentals
Mach band effect
The brightness perceived by eye is not consistent with the actual intensity !
Chapter 2: Digital Image Fundamentals Simultaneous contrast
The brightness of the object perceived by eye depends on the background !
Chapter 2: Digital Image Fundamentals Other features of HVS -- Optical illusions
Chapter 2: Digital Image Fundamentals Visible and invisible lights
Chapter 2: Digital Image Fundamentals
Image sensing
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Image acquisition and digitization
Chapter 2: Digital Image Fundamentals Image sampling and quantization
Chapter 2: Digital Image Fundamentals
Continuous image and the digitized one
Chapter 2: Digital Image Fundamentals
Representation of digital image
Chapter 2: Digital Image Fundamentals
Representation of digital image
−−−−
−−
=
)1,1()1,1()0,1(
)1,1()1,1()0,1()1,0()1,0()0,0(
),(
NMfMfMf
NfffNfff
yxf
Chapter 2: Digital Image Fundamentals
Representation of digital image
Chapter 2: Digital Image Fundamentals
Number of bits required to store a digital image
b=M×N ×k=N2 k k: # of bits for gray-level value
Chapter 2: Digital Image Fundamentals
Images with different spatial resolutions
Chapter 2: Digital Image Fundamentals Images with different gray-level resolutions
Chapter 2: Digital Image Fundamentals
Images with low gray-level resolution
Chapter 2: Digital Image Fundamentals
How the image quality is affected by varying N and k?
Three typical images used to test the quality of images with different combinations of N and k.
Chapter 2: Digital Image Fundamentals
Isopreference curve depends on image
Chapter 2: Digital Image Fundamentals
Zooming and shrinking (interpolation and decimation)
Commonly used interpolation schemes: 1) Nearest neighbor repetition
2) Bilinear interpolation
3) Bicubic interpolation
dcxybyaxyxv +++=),(
Chapter 2: Digital Image Fundamentals
Neighbors, Adjacency, Connectivity, Region, Boundary
Neighbors of a pixel 1) 4-neighbors of p, N4(p):
2) 4 diagonal neighbors of p, ND(p):
3) 8 neighbors of p, N8(p):
N8(p)=N4(p)+ND(p)
Chapter 2: Digital Image Fundamentals
Mathematical tools for DIP
Chapter 2: Digital Image Fundamentals
Arithmetic operations in DIP
Chapter 2: Digital Image Fundamentals
Arithmetic operations in DIP
Scaling:
Chapter 2: Digital Image Fundamentals
Arithmetic operations in DIP
Chapter 2: Digital Image Fundamentals
Masking of ROI
Chapter 2: Digital Image Fundamentals
Set operation
Chapter 2: Digital Image Fundamentals
Set operation
Chapter 2: Digital Image Fundamentals
Set operations
Chapter 2: Digital Image Fundamentals
Intensity transformation
Chapter 2: Digital Image Fundamentals
Neighborhood operations
Chapter 2: Digital Image Fundamentals
Geometric/spatial transformation of image
For example,
Chapter 2: Digital Image Fundamentals
Transformed images
Chapter 2: Digital Image Fundamentals
Image registration
Chapter 2: Digital Image Fundamentals
Vector product as a distance
Chapter 2: Digital Image Fundamentals
DIP in transform domain
Chapter 2: Digital Image Fundamentals
Transform-domain processed image
Chapter 2: Digital Image Fundamentals
Images with different contrasts
Example 1 (problem. 2.7)
Suppose that a flat area with center ),( 00 yx is illuminated by a light source with intensity distribution
])()[( 2
02
0),( yyxxKeyxi −+−−= Assume for simplicity that the reflectance of the area is constant and equal to 1.0, and let K=255. If the resulting image is digitized with k bits of intensity resolution, and the eye can detect an abrupt change of eight shades of intensity between adjacent pixels, what value of k will cause visible false contouring?
Example 2 (problem. 2.10)
HDTV generates images with a resolution of 1125 horizontal TV lines interlaced (where every other line is painted on the tube face in each of two fields, each field being 1/60th of a second in duration). The width-to-height aspect ratio is 16:9. The fact that the horizontal lines are distinct fixes the vertical resolution of the images. A company has designed an image capture system that generates digital images from HDTV images. The resolution of each TV (horizontal line) in their system is in proportion to vertical resolution, with the proportion being the width-to-height ratio of the images. Each pixel in the colour image has 24 bits, 8 bits each for a red, a green, and a blue image. These three primary images form a colour image. How many bits would it take to store a 2-hour HDTV program.
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