Week 1 - Image Processing Flashcards by Isabel Draper

What are the four ways we can represent an image

Image Function
Landscape
Array of pixels
Image histogram

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What is a grayscale image function

I : R^2 - > R
(x,y) -> I(x,y)
Gives the Intensity at position I(x,y)

A digital image is a discrete (sampled) version of this function

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What is an image landscape

Imagine throwing a piece of cloth over a scene
creates a smooth and continuous landscape

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What is an image histogram

Count the amount of pixels at each grey level

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What is Spatial Resolution

The number of pixels that determines the resolution
(want more detail -> need more pixels)
eg Pixels per inch
-Allows for smaller scale structured images (to be seen in detail)
-constantly increases in digital cameras

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What is grey level resolution

Number of different shades of grey that can be represented in an image

Higher resolution - > captures fine changes in brightness

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What is important about having an array of pixels representation

We get the spatial relationship

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What is Image arithmetic

Considering images as functions
g(x,y) = f(x,y) + 20
brightens by 20

g(x,y) = f(-x,y)
image is inverted along the y axis

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What is image addition and what does it do visually

Takes the average over images in a sequence
(adding the two corresponding pixels)
Reduces noise

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What is image subtraction and what does it do visually

Takes the difference of two images
Can capture a movement between two static background (the leftover is the moving object in the image)
Captures, shadows, reflections

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What is Noise

A stochastic (random) process
All images contain noise

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Do identical images have the same noise

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How do we model noise

As small fluctuations
Noise is normally distributed
Some small fluctuations may actually be small changes in an image and not noise

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What is the signal-to-noise ratio

SNR = max signal / σ

σ has subscript noise
Larger σ creates a smaller ratio and a more noisy image

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How do we reduce noise by temporal averaging

We can average noise over N images

σ(subscript N) = σ / √N

Take N consecutive samples of the same image
the noise reduces

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How do we reduce noise by spatial averaging

(When we do not have the choice of sampling an image n times)
Pass a 3x3 mask over an image, replacing the central pixel with the average of neighbours
“local averaging”

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What is the issue with reducing noise by spatial averaging

Lose resolution - like blurring 5x5
particularly along edges

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What is sub-sampling

Throw away every other row and columns in the image to create a 1/2 size image
Creates a very scruffy output

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How can we improve sub-sampling

Local average first (which removes small scale detail and noise) then subsample
Means we are not keeping noise

What are the 3 ways we can define noise using its neighbours

Noisy dark areas
-just a messy area of dark pixels

Just noise
-white pixels with one dark pixels (one noise pixel)

Vertical edge
-noise is aligned vertically

What is neighbourhood processing and the 2 main methods

Modifying the pixel values of an image based on the values of neighboring pixels in a window

1) Rank filtering
2) Convolution

What is Rank filtering

Takes values in the filter window and orders them darkest to lightest
There are different ways to rank: minimum (erosion), maximum (dilation), median filtering
Replace the middle pixel with whichever chosen method (minimum/median/maximum)
Move to the next window and repeat

What is the effect of median filtering

Removes both dark and light noise spots
It is not the same as taking the mean
Mean -> creates shades of grey
Median - > produces only either white or black, keeps sharp edges
Works very well for salt and pepper noise

What is Convolution

Very often used in pre-processing
Want to maintain the spatial patterns
Uses a weighted filter window

Eg 1 2 1

applied to input image: 0.5 1 1

only changing the middle value to the average of all
The middle value = 0.9

NOTE divide by 4 (total of filter weight) not by pixel number

What is a kernel

filter/mask/convolution mask/ window

What is asterisk notation for convolution

I~ = g * I or I~ = I * g g = kernel weights I = input image I~ = output image

Why does convolution create a cropped output image

We cannot begin in the corner pixel as it does not have a full set of 3x3 neighbors

What are 3 padding methods

Zero: set all pixels outside image to 0 constant (border colour): set all pixels outside source image to specific value mirror: reflect pixels across the image edge

What is linear filtering

output is a linear combination of the input pixel values within a local neighborhood

What is a smoothing kernel

9x9 mask filled with 1s (constant mask) ___|-|___

What is a gaussian kernel

Smoothing using a normal distribution Any kernel representing a single bump distribution will blur the image 1 2 1 2 4 2 1 2 1

How does changing sigma affect the gaussian filter

small σ = sharper, more weight to central pixels, less blur large σ = weight spread, more blurred

What is segmentation

The division into background pixels and object pixels grayscale image -> binary label image Only applicable in simple, high contrasting images

What is thresholding

b(x,y) = (g(x,y) < T) If we want the dark pixels (objects of interest) b(x,y) is a binary image We can use two thresholds if we want two different gray levels

What is a bimodal image histogram

A histogram that shows two distinct levels background/foreground

What is the intermodal minimum

Where we place the threshold marks in between background and foreground pixel values

Histogram as a derivative of area

H(D) = dA(D) /dD where D = grey level Because the area under the histogram curve represents the total intensity

What is Adaptive Thresholding

When we encounter shaded backgrounds and a single threshold wont work Smooths first to get estimate of varying background Subtract smoothed image from the original Then threshold

What is over/under segmentation

Thresholding too high(over) or low(under) causing a poor quality output image

How do we carry out automated thresholding selection

There are many algorithms that do this

What does having longer words at each pixel allow

Each pixel can display a greater range of grey values (bits per pixel)

what is a word

basic unit of data that can process a single operation size of word = __bits commonly 1 byte = 8 bits

What do lighter pixels in grayscale images represent

objects (why dilation = choosing maximum Intensity pixel)

Is convolution linear filtering

Yes

Is median filtering linear filtering