Image negative
Log transformation
Power-law (gamma) transformation
Piecewise-linear transformation functions
o Contrast stretching
o Intensity level slicing
o Bit plane slicing
o Contrast stretching
o Intensity level slicing
o Bit plane slicing
Histogram processing
Spatial domain processes denoted by: g(x,y)=T[f(x,y)]
Histogram processing
Histogram of an image shows us the distribution of grey levels in an image
Histogram of a digital image is a discrete function: h(rk)=nk
Normalized histogram p(rk)=nk/MN , for k=0,1,2,…, L-1
Sum of all components of a normalized histogram is equal to 1
High contrast image covers a wide range of the intensity scale and pixels distribution is almost uniform
Histogram equalization automatically determines a transformation function that produces and output image that has a near uniform histogram
Spatial domain processes denoted by: g(x,y)=T[f(x,y)]
It is an image plane, direct manipulate of pixel in an image.
Move from pixel to pixel to compute the average intensity of the neighborhood. The above application is also called spatial filtering.
Single pixel neighbourhood or point processing techniques
- Simplest form of T
- Smallest possible neighbourhood of size 1 x 1
- Gray level transformation s=T(r)
- Thresholding
- Contrast stretching
Image negatives: input (0, L-1) transformation: s= L-1-r
Log transformations: s= c log (1+r)
Maps a narrow range of gray level values in input image to a wider range of output levels, or the other way round with inverse log transform
Power-law transformations: s= c*r^y
Map a narrow range of dark input values into a wider range of output values or vice versa
Piecewise linear transformation functions
Move from pixel to pixel to compute the average intensity of the neighborhood. The above application is also called spatial filtering.
Single pixel neighbourhood or point processing techniques
- Simplest form of T
- Smallest possible neighbourhood of size 1 x 1
- Gray level transformation s=T(r)
- Thresholding
- Contrast stretching
Image negatives: input (0, L-1) transformation: s= L-1-r
Log transformations: s= c log (1+r)
Maps a narrow range of gray level values in input image to a wider range of output levels, or the other way round with inverse log transform
Power-law transformations: s= c*r^y
Map a narrow range of dark input values into a wider range of output values or vice versa
Piecewise linear transformation functions
Contract stretching: expands the range of intensity levels in an image. Increase the dark and lights contrast
Intensity level slicing: highlight specific range of gray levels in an image. There are two type: Ist: interested change to white, the rest all black. Second, brighten the interested part, the rest remain unchanged.
Bit place slicing: highlight specific bits
Histogram processing
Histogram of an image shows us the distribution of grey levels in an image
Histogram of a digital image is a discrete function: h(rk)=nk
Normalized histogram p(rk)=nk/MN , for k=0,1,2,…, L-1
Sum of all components of a normalized histogram is equal to 1
High contrast image covers a wide range of the intensity scale and pixels distribution is almost uniform
Histogram equalization automatically determines a transformation function that produces and output image that has a near uniform histogram
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