1. Introduction to Grayscale and Color Images
Image acquisition
Light and Electromagnetic spectrum
Charge-Coupled Device (CCD) imaging and Bayer Pattern (the most popular color-filter-array)
Sampling and Quantization
Image representation
Spatial resolution
Bit-depth resolution
Local neighborhood
Block decomposition
EE465: Introduction to Digital Image Processing

2. Electromagnetic spectrum
EE465: Introduction to Digital Image Processing

3. Light: the Visible Spectrum
Visible range: 0.43µm(violet)-0.78µm(red)
Six bands: violet, blue, green, yellow, orange, red
The color of an object is determined by the nature of the light reflected by the object
Monochromatic light (gray level)
Three elements measuring chromatic light
Radiance, luminance and brightness
EE465: Introduction to Digital Image Processing

4. Sensor Array: CCD Imaging
EE465: Introduction to Digital Image Processing

5. Charge coupled device (CCD) image sensor
EE465: Introduction to Digital Image Processing

6. EE465: Introduction to Digital Image Processing

7. Complementary Metal Oxide Semiconductor (CMOS) Image Sensor
EE465: Introduction to Digital Image Processing

8. EE465: Introduction to Digital Image Processing
Image Formation Model
f(x,y)=i(x,y)r(x,y)+n(x,y)
0<f(x,y)<∞
Intensity – proportional to energy
radiated by a physical source
0<i(x,y)<∞
illumination
0<r(x,y)<1
reflectance
(“intrinsic images”)
n(x,y)
noise
EE465: Introduction to Digital Image Processing

9. Sampling and Quantization: 1D Case
EE465: Introduction to Digital Image Processing

10. 2D Sampling and Quantization
EE465: Introduction to Digital Image Processing

11. EE465: Introduction to Digital Image Processing
3D Visualization
It is useful to take an analogy to rain gauge (image intensity values
Measure the amount of ``photon rain’’)
EE465: Introduction to Digital Image Processing

12. Color Imaging: Bayer Pattern
$38,990
$309
US3,971,065
EE465: Introduction to Digital Image Processing

13. Demosaicing (CFA Interpolation)
EE465: Introduction to Digital Image Processing

14. Simple Ideas: Linear Interpolation
You will be asked to try these simple ideas in CA#2
EE465: Introduction to Digital Image Processing

15. Biological vs. Artificial Sensors
Cone distribution in human retina
US3,971,065
Question: Engineers’ invention vs. nature’s evolution, who wins?
EE465: Introduction to Digital Image Processing

16. Digital Single-Lens Reflection (DSLR) Cameras
EE465: Introduction to Digital Image Processing

17. EE465: Introduction to Digital Image Processing
Nikon D50
EE465: Introduction to Digital Image Processing

18. EE465: Introduction to Digital Image Processing
Kodak Easyshare
EE465: Introduction to Digital Image Processing

19. EE465: Introduction to Digital Image Processing
Photography 101
pros
Interchangable lens
Greater quality and lower noise
Suitable for high-motion and low-light environment
Better focusing capability
Larger focal length
Cons
Larger and heavier
More expensive
Lack of video mode
Sensor dust problem
More difficult to focus on very close objects
EE465: Introduction to Digital Image Processing

20. The Plague in Photography: Motion Blur
EE465: Introduction to Digital Image Processing

21. High Dynamic Range Imaging
Q: Can we generate a HDR image (16bpp) by a standard camera?
A: Yes, adjust the exposure and fuse multiple LDR images together
EE465: Introduction to Digital Image Processing

22. HDR Display (After Toner Mapping)
Note that any commercial display devices we see these days are NOT HDR
EE465: Introduction to Digital Image Processing

23. EE465: Introduction to Digital Image Processing

24. EE465: Introduction to Digital Image Processing
Beyond Visible
Gamma-ray and X-ray: medical and astronomical applications
Infrared (thermal imaging): near-infrared and far-infrared
Microwave imaging:
Radio-frequency: MRI and astronomic applications
EE465: Introduction to Digital Image Processing

25. Thermal Imaging Operate in infrared frequency Grayscale representation
(bright pixels correlate with
high-temperature regions)
Pseudo-color representation
(Human body dispersing
heat denoted by red)
EE465: Introduction to Digital Image Processing

26. Low Signal-to-Noise (SNR) Behavior
EE465: Introduction to Digital Image Processing

27. EE465: Introduction to Digital Image Processing
Radar Imaging
Operate in microwave frequency
Mountains in Southeast Tibet
EE465: Introduction to Digital Image Processing

28. Synthetic Aperture Radar (SAR)
Environmental monitoring, earth-resource mapping, and military systems
SAR imagery must be acquired in inclement weather and all-day-all-night.
SAR produces relatively fine azimuth resolution that differentiates it from other radars.
EE465: Introduction to Digital Image Processing

29. Magnetic Resonance Imaging (MRI)
Operate in radio frequency
knee
spine
head
EE465: Introduction to Digital Image Processing

30. EE465: Introduction to Digital Image Processing
Basic Principle of MRI
k-space
IFT
EE465: Introduction to Digital Image Processing

31. Comparison of Different Imaging Modalities
visible
infrared
radio
EE465: Introduction to Digital Image Processing

32. Fluorescence Microscopy Imaging
Operate in ultraviolet frequency
normal corn
smut corn
EE465: Introduction to Digital Image Processing

33. What Does a Neuron Look Like?
Artistic illustration
Real image
EE465: Introduction to Digital Image Processing

34. EE465: Introduction to Digital Image Processing
X-ray Imaging
Operate in X-ray frequency
chest
head
EE465: Introduction to Digital Image Processing

35. Positron Emission Tomography
Operate in gamma-ray frequency
EE465: Introduction to Digital Image Processing

36. Mechanical Categorization of Sensors
Motionless imaging
Sensor is kept still during the acquisition (e.g., CCD cameras)
Motion-aided imaging
Sensor moves along a line or rotates around a center during the acquisition (e.g., document scanning and MRI scanning)
Subtle relationship between visual perception and motion
“We move because we see; we see because we move” – J. Gibson
EE465: Introduction to Digital Image Processing

37. Single-sensor Imaging
EE465: Introduction to Digital Image Processing

38. EE465: Introduction to Digital Image Processing
“Motion” Aids Imaging
EE465: Introduction to Digital Image Processing

39. EE465: Introduction to Digital Image Processing

40. EE465: Introduction to Digital Image Processing
Introduction to Grayscale Images
Image acquisition
Light and Electromagnetic spectrum
Charge-Coupled Device (CCD) imaging
Sampling and Quantization
Image representation
Spatial resolution
Bit-depth resolution
Local neighborhood
Block decomposition
EE465: Introduction to Digital Image Processing

41. Image Represented by a Matrix
Spatial resolution
Bit-depth resolution
EE465: Introduction to Digital Image Processing

42. EE465: Introduction to Digital Image Processing
Spatial Resolution
EE465: Introduction to Digital Image Processing

43. EE465: Introduction to Digital Image Processing
Image Resampling
EE465: Introduction to Digital Image Processing

44. EE465: Introduction to Digital Image Processing
Towards Gigapixel
Mega-pel
Giga-pel
Photographers and artists have manually or semi-automatically
stitched hundreds of mega-pel pictures together to demonstrate
how a giga-pel picture looks like  the power of pixels
EE465: Introduction to Digital Image Processing

45. Aliasing in Digital Images
EE465: Introduction to Digital Image Processing

46. EE465: Introduction to Digital Image Processing
Bit-depth Resolution
EE465: Introduction to Digital Image Processing

47. Bit-depth Resolution (Con’d)
EE465: Introduction to Digital Image Processing

48. EE465: Introduction to Digital Image Processing
Commonly–used Terminology
Neighbors of a pixel p=(i,j)
N8(p)={(i-1,j),(i+1,j),(i,j-1),(i,j+1),
(i-1,j-1),(i-1,j+1),(i+1,j-1),(i+1,j+1)}
N4(p)={(i-1,j),(i+1,j),(i,j-1),(i,j+1)}
Adjacency
4-adjacency: p,q are 4-adjacent if p is in the set N4(q)
8-adjacency: p,q are 8-adjacent if p is in the set N8(q)
Note that if p is in N4/8(q), then q must be also in N4/8(p)
EE465: Introduction to Digital Image Processing

49. Common Distance Definitions
D8 distance
(checkboard distance)
Euclidean distance
(2-norm)
D4 distance
(city-block distance) 2 4 3 2 3 4 2 2 2 2 2 1 3 2 1 2 3 2 1 1 1 2 2 1 1 1 2 2 1 2 2 1 1 2 1 3 2 1 2 3 2 1 1 1 2 2 4 3 2 3 4 2 2 2 2 2
EE465: Introduction to Digital Image Processing

50. Block-based Processing
EE465: Introduction to Digital Image Processing