Image Processing 1 + 2

Question 1

Digitization

Answer 1

Limited x resolution (Sampling) and Limited y resolution (Quantization)

Question 2

Spatial Quality

Answer 2

Pixel size

Question 3

Intensity Quality

Answer 3

Grey scale bit level

Question 4

Nyquist-Shannon Theorem

Answer 4

Min. sampling rate = 2f, lower means details are lost (like clothing on tv) and new patterns can arise

Question 5

Convolution/Kernel

Answer 5

CONVOLUTION is the process of applying a KERNEL to the pixels of an image

Question 6

Dirac/Delta Kernel

Answer 6

Kernel width approaches 0 (and height infinity)

Question 7

Mach Band Effect

Answer 7

How the brain enhances contrast

Question 8

Aliasing

Answer 8

High frequency patterns become (different) low frequency patterns

Question 9

Point Spread Function (PSF)

Answer 9

Determines the optical resolution, the smallest spatial detail visible

Question 10

Gamma correction

Answer 10

0-1 = lighter
1+ = darker

Question 11

More contrast (l)

Answer 11

• Contrast stretching (slope streched)
• Windowing (area with slope narrowed _/-)
• Thresholding (instant slope _|-)
• Double thresholding/window slicing (|-|)

Question 12

Histogram Equalization

Answer 12

Remap grey values so they all occur in equal numbers (can only be approached for discrete, digital images)

Question 13

Ideal pixels per bin

Answer 13

Npixels/Ngreyscales

Question 14

Cumulative histogram

Answer 14

Number of pixels with at MOST a grey value x

Question 15

Grey scale substraction/addition

Answer 15

Shows change/filters out noise after dividing by amount of images added

Question 16

2D Histogram

Answer 16

The grey values in the 2D histogram indicates how often certain combinations of grey values occur

Question 17

Noise Filters

Answer 17

• Salt and Pepper (adds some black and white)
• Uniform (equally around the range of the original)
• Gaussian

Question 18

Convolution/Correlation

Answer 18

Cross-multiplication/normal multiplication
Convolution has laws, correlation doesn’t:
-Cummutative law (order filter/image irrelevant)
-Associative law (order of multiple filters irrelevant)
-Distributive law gf+hf=(g+h)f

Question 19

Filter near image border

Answer 19

-Fixed values
-Extrapolation
Mirroring

Question 20

Low pass/High pass filtering

Detail enhancement/Unsharp masking

Answer 20

Low pass: Reduces noise
High pass: image - low pass, (sums up to 0 instead of 1 ,so constant areas vanish)

Detail enhancement: high pass, but adds up to 1 (constant areas stay the same, contrast enhanced)
Unsharp masking: + a high pass filtered image or - a low pass filtered image