Image processing 3
Hi !
Today i will talk about ;
Intensity transformations and spatial filtering
Spatial domain processing :
1) intensity (gray level) transformations
2) spatial filtering
g (x,y) = T [f (x,y)] : general form of spatial filtering
f is the input image and g is the output image , T is an operator on f , defined over a specified neighbourhood about point (x,y) .
when we work with color images , intensity is used to denote a color image component certain color spaces .
Function imadjust
A basic image processing toolbox tool for intensity transformations of gray scale .
G = imadjust (f,[low_in high_in],[low_out high_out], gamma)
This function maps the intensity values in image f to new values in g , such that values between low_in and high_in values between low_out and high_out values .
Values less than low_in and above high_in clipped . It means that values above high_in map to high_out and values less than low_in clipped to low_out .
The input and output image is the same class; unit8 unit 16 etc .
>> f = imread (‘40977.jpg’);
>> g = imadjust(f,[0 1],[1 0]);
>> imshow (g)
This is the digital equivalent of obtaining a photographic negative , useful for enhancing white or gray detail embedded in a large , predominantly dark region .
Negative of an image can also be called with imcomplement function.
>> g = imcomplement(f);
>> imshow (g);
Ex :
>> g2 = imadjust (f,[0.5 0.75],[0 1]);
>> imshow (g2)
This example expands the gray scale region 0.5 and 0.75 to the full 0 1 range . It is great for highlighting an intensity band of ineterest .
Ex :
>> g3 = imadjust (f,[],[],2);
>> imshow (g3)
More gray tones …
Logarithmic and ContrastStretching Tranformations
Basic tools for dynamic range manipulation .
G = c*log(1+double(f))
C is a constant . Shape is similar to the gamma curve .
It is also used to compress dynamic range . For example , it is not unsual to have a Fourier spectrum with values in the range [0 10^6] or higher . When we display it on the monitor , it is scaled linearly to 8 bits , high values dominate the display , resulting in lost visual detail for lower intensity values in the spectrum .
By computing the log , a dynamic range on the order of , for example 10^6 , is reduced to approximately 14 , which is much more manageable .
While working logarithmic transformation , it is often desirable to bring the resulting compressed values back to the full range display .
For 8 bits , it is easily done like that :
G4= im2unit8 (mat2gray(g)); % using mat2gray brings the values to the range [0 1] and im2unit8 brings them to the range [0 255]
Later this transformations will be easier .
ContrastStretching Function
Compresses the input levels lower than m into a narrow range of dark levels in the output image ; it compress the values above m into a narrow band of light levels in the output . Result is an image of higher contrast .
This limiting function called thresholding function , a simple tool used for image segmentation .
S = T(r) = 1/ (m/r)^E
E controls the slope of the function .
We can implement this in Matlab like that ;
G = 1./(1+double(f)+eps)).^E
(suppose E is 20)
Use of eps prevents any overflow if f has any 0 values
See you !

Recent

Bağlantılar

Arşivler
 Ağustos 2011 (1)
 Temmuz 2011 (1)
 Ocak 2011 (1)
 Aralık 2010 (2)
 Kasım 2010 (4)
 Ekim 2010 (6)
 Eylül 2010 (7)
 Ağustos 2010 (9)

Kategoriler

RSS
Entries RSS
Comments RSS