The principal objective of enhancement technique is to process a given image so that the result is more suitable than the original image for a specific application. Image enhancement techniques are mainly classified into two broad categories: Spatial domain method and Frequency domain method.

Spatial Domain Methods are as follows:

a) EnhancementbyZeroMemory Point Operations

• In zero memory operation, output image pixel value is obtained directly processing the input image pixel value. For every input image pixel value, transformation function gives corresponding output image pixel value and no memory location is required to store intermediate results.
• The various Zero Memory Point operations are:

1. Contrast Stretching Transformation
2. Digital Negative Transformation
3. Logarithmic Transformation
4. Power Law Transformation
5. Intensity Level Slicing Transformation and
6. Bit Level Slicing Transformation.

b) Enhancement by Histogram Processing

Histogram processing involves the modification of input image histogram so as to improve the visual quality of image on display device.

There are three approaches of histogram processing as follow: - Histogram Equalization: It is a process that attempts to spread out the gray levels in an image so that they are evenly distributed over the entire range. The histogram of the output image is almost uniform over the entire range of gray levels. It provides only one type of output. - Histogram Specification: It is a process that attempts to spread out the gray levels in an image as per the specified image histogram. Modifies histogram of the input image closely matches with the histogram of the specified image. - Histogram Stretching: It is a process that attempts to spread out the gray levels in an image linearly as per the required range of output image histogram.

c) Enhancement by Neighborhood Processing: Spatial Filtering

Special filtering involves passing a weighted mask or kernel over the image and replacing the original image pixel value corresponding to the centre of the kernel with the sum of the original pixel values in the region corresponding to the kernel multiplies by the kernel weight.

• Smoothing Linear Filters: Examples of linear filters are Low Pass Averaging Filter, Weighted Average Filter and Trimmed Average Filter.
• Smoothing Non-Linear Filter: Non-linear filters are also called as Ordered Statistic Filters. Examples are Median, Max and Min Filter.
• Sharpening First Order Derivative Filters: Examples are Robert, Prewit, Sobel and Fri-Chen Filter.
• Sharpening Second Order Derivative Filters: Examples are Palladian Filter, High Pass Filter and High Boost Filter.

d) Magnification and zooming.