Write a note on digital image processing
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Write a note on digital image processing?

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1). An image may be defined as a two-dimensional function, , where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point.
2). When x, y, and the intensity values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer
3).Importance and necessity of digital image processing stems from two principal application areas: the first being the Improvement of pictorial information for human interpretation and the second being the Processing of a scene data for an autonomous machine perception.
4). The Image processing Procedures such as Image enhancement and restoration are used to process degraded or blurred images.
4.1). Image Enhancement: 
The principle objective of image enhancement technique is to process an image so that the resultant image is more suitable than the original for a particular application. Most of the enhancement techniques are very much problem oriented and hence enhancement for one application may turn out to be degradation for the other. Enhancement approaches may be classified especially into two broad categories. 
1. Spatial domain enhancement techniques 
2. Frequency domain enhancement techniques. 
The former technique refers to process the image in the image plane (pixels) itself while the latter techniques are based on modifying the transform (Fourier or any other) of an image. In most of the general enhancement techniques for problems involve various combinations of methods from both the categories. Some examples of enhancement operations are edge enhancement, pseudocoloring, histogram equalization(HE), contrast stretching, noise filtering, un-sharp masking, sharpening, magnifying, etc. 
Usually the enhancement process does not increase the inherent information content present in the image but only tries to present it in a suitable manner for easy assessment. These image enhancement operations may be either local or global. Global operations work on the entire image at a time while local operations define spatial masks i.e., on small subimages over which the operation is to be performed.

Image Segmentation : 
Image segmentation is the most sought after technique for extracting information from an image. This is generally considered as the first step in image analysis. 
The Segmentation process subdivides an image into its constituent parts or objects, such that level of subdivision depends on the problem to be solved. 
Segmentation is stopped when the Region of interest in a specific application has been isolated. Generally one of the most difficult tasks in digital image processing is the autonomous segmentation method. This step determines the eventual success or failure of the image analysis. Effective segmentation very rarely fails to lead to a successful solution. 
The application of segmentation algorithms on monochrome images generally are based on one of the two basic and important properties of gray level values 
1. Discontinuity
 2. Similarity In discontinuity,
 the simple approach is to partition an image is basically based on changes that occur abruptly in gray level. 
The principal areas of image processing 7 interest within this type of discontinuity are detection of isolated points, lines and edges in an image. 
The principal approaches in the second type are based on thresholding, region growing, splitting and merging. The concept of segmentation algorithms based on discontinuity or similarity of the gray level value of its pixels is applicable to both static and dynamic images.


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