Image enhancement is among the simplest and most appealing areas of digital image processing. Image enhancement is used to bring out details that are obscured(keep from being seen) or simply to highlight certain features of interest in image. Image enhancement is a very subjective area of image processing
Image enhancement approaches fall into two broad categories
- spatial domain methods
- frequency domain methods
spatial domain methods
In image enhancement spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image.
frequency domain methods
in image enhancement frequency domain methods techniques are based on modifying the Fourier transform of an image. Enhancing an image provides better contrast and a more detailed image as compare to non enhanced image
Spatial domain processes syntax
g(x,y) = T[f(x,y)]
where f(x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y), The center of the subimage is moved from pixel to pixel starting, say, at the top left corner. The operator T is applied at each location (x, y) to yield the output, g, at that location. The process utilizes only the pixels in the area of the image spanned by the neighborhood.
BASIC GRAY LEVEL TRANSFORMATIONS:
- Imagenegative
- Logtransformations
- Power lawtransformations
- Piecewise-Linear transformationfunctions
LINEAR TRANSFORMATION:
Linear transformation includes simple identity and negative transformation.
Identity transition
In this transition, each value of the input image is directly mapped to each other value of output image. That results in the same input image and output image. And hence is called identity transformation.
NEGATIVE TRANSFORMATION:
Negative transformation, which is invert of identity transformation. In negative transformation, each value of the input image is subtracted from the L-1 and mapped onto the output image
L is the largest gray level consists in the image
IMAGENEGATIVE:
The imagenegativewithgraylevelvalue inthe rangeof[0,L-1]isobtained bynegative transformation given by S = T(r)
Where T(r)=S = L -1 – r
Where r= gray level value at pixel (x,y)
LOGARITHMIC TRANSFORMATIONS:
.Logarithmic transformation further contains two type of transformation. Log transformation and inverse log transformation.
LOG TRANSFORMATIONS:
The log transformations can be defined by this formula
s = c log(r + 1).
Where s and r are the pixel values of the output and the input image and c is a constant. The value 1 is added to each of the pixel value of the input image because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity
During log transformation, the dark pixels in an image are expanded as compare to the higher pixel values. The higher pixel values are kind of compressed in log transformation.
POWER – LAW TRANSFORMATIONS:
There are further two transformation is power law transformations, that include nth power and nth root transformation. These transformations can be given by the expression:
s=cr^γ
This symbol γ is called gamma, due to which this transformation is also known as gamma transformation
- γ > 1 compresses dark values (Expands bright values)
- γ <1(similarto Logtransformation) Expands dark values Compresses brightvalues
When C = γ = 1 , it reduces to identity transformation
Piecewise-Linear Transformation Functions:
- Contrast stretching:One of the simplest piecewise linear functions is a contrast-stretching transformation. Low-contrast images can result from poor illumination, lack of dynamic range in the imaging sensor, or even wrong setting of a lens aperture during image acquisition
S= T(r )
Gray-level slicing:
Highlighting a specific range of gray levels in an image often is desired. Applications include enhancing features such as masses of water in satellite imagery and enhancing flaws in X-ray images. There are several ways of doing level slicing, but most of them are variations of two basic themes. One approach is to display a high value for all gray levels in the range of interest and a low value for all other gray levels.