chapter 3- gonzalez

Digital Image Processing Using MATLAB®

Chapter 3Intensity Transformations

and Spatial Filtering

( , ) [ ( , )]g x y T f x y• 3.1 Background



and Spatial Filtering• 3.2 Intensity transformation functions

– Simplest: voxel-wise operator (1x1 mask)

( , ) [ ( , )] ( )g x y T f x y s T r

g=imadjust(f,[low_in, high_in], [low_out, high_out, gamma])

Digital Image Processing Using MATLAB®Some Intensity Transformation Functions

Digital Image Processing Using MATLAB®Power–Law (Gamma) transformation

s = crγ, c,γ –positive constantscurve the grayscale components either to brighten the

intensity (when γ< 1) or darken the intensity (when γ > 1).

Digital Image Processing Using MATLAB®Power –Law (Gamma) transformation



and Spatial Filtering• 3.2 Intensity transformation functions (cont)

– Logarithmic transformation of intensities– Contrast stretching 1( )

1Es T r

mr

g = 1 ./ (1 + (m ./ (double(f) + eps )) .^ E);


Contrast stretchingContrast stretching is a process that expands the range of intensity

levels in a image so that it spans the full intensity range of the recording medium or

display device.Contrast-stretching transformations increase the contrast between the

darks and the lights


Thresholding function


Intensity-level slicingHighlighting a specific range of gray levels in an

image


Histogram processingThe histogram of a digital image

with gray levels in the range [0, L-1] is

a discretefunction h(rk)=nk , where rk is the

kth gray level and nk is the number of

pixels in the image having gray level rk. It is common practice to

normalize a histogram by dividing each of its

values by the total number of pixels in the

image, denoted by the product MN.

Thus, a normalized histogram is given by h(rk)=nk/MN

The sum of all components of anormalized histogram is equal to

1.


Histogram Equalization Histogram equalization can be used to improve the visual

appearance of an image.

Histogram equalization automatically determines a transformation function that produce and output image that has a near uniform histogram


Histogram EqualizationLet rk, k[0..L-1] be intensity levels and let

p(rk) be its normalized histogram function.The intensity transformation function for

histogram equalization is

k

jj

k

jjrkk

LknMNL

rpLrTs

0

0

1,...,2,1,0,1

)()1()(




g=histeq (f, nlev)




g=imfilter (f, w, filtering_mode, boundary_opts, size_opts)


Chapter 3Intensity Transformationsand Spatial Filtering

w=fspecial (type , [params])

chapter 3- gonzalez

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