Linear Filters Flashcards

1
Q

How does the process of spatial filtering differ from point processes?

A

Change is made to one pixel at a time BUT a whole window/kernel surrounding the source pixel is considered

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What is image noise?

A

Small errors in image values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How is noise often modelled?

A

recorded value = true value + random noise value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What are the two main ways noise is added to an image?

A

During image capture, and during image compression

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What is the concept of gaussian noise?

A

Sensors often measure a value as a little off, but on average they are right, and generally the values are closer to true rather than further. So we can model noise as a gaussian distribution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What is the mean added noise value?

A

0

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What is convolution filtering?

A

A process where a filter is run over parts of an image covering a window at a time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What are some negatives of using the mean filter for noise reduction?

A

Hard to use around the edges where there are not as many surrounding pixels

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What is gaussian filtering?

A

Convolution filtering but with a mask whose weights are determined by a 2D gaussian function (e.g higher weight is given to pixels closer to the source pixel)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What does the volume under a 2D gaussian add up to?

A

1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What does a gaussian filter look like?

A

A grid of numbers with the largest in the middle

How well did you know this?
1
Not at all
2
3
4
5
Perfectly