Image enhancement in spatial domain is used for:

1. Improving the interpretability or perception of information in images for human viewers

2. Providing `better' input for other automated image processing techniques

Spatial domain methods operate directly on pixels.

Various spatial domain methods are as follows:

1. Point Processing

The simplest kind of range transformations are these independent of position x,y:

g = T(f)

This is called point processing.

Note: for every pixel, spatial information completely lost.

2. Negative image

Negative images are useful for enhancing white or grey detail embedded in dark regions of an image

3. Log transform

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. So 1 is added, to make the minimum value at least 1.

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. This result in following image enhancement.

4. Power law transform

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.

Variation in the value of γ varies the enhancement of the images. Different display devices / monitors have their own gamma correction, that’s why they display their image at different intensity.

This type of transformation is used for enhancing images for different type of display devices. The gamma of different display devices is different. For example Gamma of CRT lies in between of 1.8 to 2.5 that means the image displayed on CRT is dark.