Spatial Image Enhancement Flashcards
1
Q
Image enhancement
A
- Transformations that highlight image features
- mathematical manipulations of data
- new data created from raw data
2
Q
Fourier methods
A
- Performed in the frequency
3
Q
9-point smoothing filter
A
- applied to all central pixels, not pixels around edges
- convolution kernal (mask) makes pixels more alike
4
Q
Weighted smoothing filter
A
- convolution kernel is weighted
- pixel weight determined by proximity to central pixel
- less blurring and better spatial resolution compared to unweighted smoothing
- AKA replaces the averaging
- new pixel value= Σ(wt. x pixel value)/16(total wt. of kernal)
5
Q
Edge enhancement filter
A
- opposite to image smoothing
- uses the same pixel replace averaging technique
- negative coefficients used in weighting
- kernel enhances edges, increasing contrast.
6
Q
Mask Size
A
- The mask or kernel always has an odd number of rows and columns of pixels (edge enhancement and smoothing)
- Dimensions vary according to the equation (2n+1) x (2n+1)
- 3x3, 5x5, 7x7
7
Q
Point processing operations
A
- Manipulations of the number of cts/pixel
- completely independant of neighbouring pixels
- background subtraction, gray scales, and color tables
8
Q
Background subtraction
A
- Neighbourhood independant
- subtracting pre a determined, constant number of counts from each pixel in an image
- Improves contrast, but comes at the cost of increased noise
9
Q
Interpolated background subtraction
A
- neighbourhood dependant
- does not assume that background is uniform, but varies with position in the image
- creates an ROI, all pixels outside ROI are considered background
- background pixels are weighed according to their distance from ROI
10
Q
Gray scale
A
- neighbourhood independant, referred to also as the dynamic range
- number of shades between complete black and complete white
- translating the number of counts in a pixel to an integer, which in turn defines the pixels color
11
Q
Linear Gray Scale
A
- Gray scale assigned is proportional to the number of counts in the pixel
- The pixel with the highest number of counts is assigned white and the pixel with no counts is assigned black
- all other pixels are given a gray scale proportional to the ratio of counts in each pixel to the maximum counts per pixel
- Normalizes pixel counts
12
Q
Non-normalized gray scale
A
- Better suited for low count images
- causes loss of image contrast in high count images
- Limits dynamic range
- If hottest pixel is 200 counts, the gray level is 200
- all pixels are given the shade that corresponds to the absolute count/pixel value
13
Q
Thresholding gray scale
A
- a variation of linear gray scale
- eliminates background
- cuts off lower percentage of maximum counts
- all pixels less than a set percentage of maximum counts will appear black and the gray scale will increase from there
14
Q
Logarithmic and exponential gray scale
A
- grey scales can be logarithmic or exponential in design
- logarithmic enhance image contrast in low count areas and decreases it in high count areas
- exponential is opposide to logarithmic
- both should be used cautiously as they can result in false interpretation of RP distribution
15
Q
color translation tables
A
- lookup tables used to assign colors or different intensities of the same color to each pixel as a function of pixel counts
- thre intensity scales are used: red, green, blue
- user preference
