Image Processing

    16 Votes

In the present world Computer Graphics plays an important role. The areas here we are using computer graphics are Entertainment, Presentations, Education and training, Visualization, Design, Image Processing and Graphical User Interface. In all these Image Processing has its own importance. Image Processing deals with how we can improve the clarity of image and to manipulate the image which is a very important application of computer graphics. In Image processing we are doing some operation on image.

This paper mainly concentrates on what is an image and how processing takes place, digital image. It also deals with Characteristics of image operations like types of operations and types of neighbourhood, video parameters, statistics of images, contour representations like chain code, crack code, run code. This paper also deals with Noise that contaminates the images acquired from modern sensors and one of the main applications of Image Processing that is cameras.

Image Processing

Modern digital technology has made it possible to manipulate multi-dimensional Signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories

  • Image Processing image in image out
  • Image Analysis image in measurements out
  • Image Understanding image in high-level description out

An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y). An image may be considered to contain sub-images sometimes referred to as regions of interest (ROI) or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.

The amplitudes of a given image will almost always be either real numbers or integer numbers. The latter is usually a result of a quantization process that converts a continuous range to a discrete number of levels. In certain image-forming processes, however, the signal may involve photon counting which implies that the amplitude would be inherently quantized. In other image forming procedures, such as magnetic resonance imaging, the direct physical measurement yields a complex number in the form of a real magnitude and a real phase.

Digitization of continuous image

A digital image a[m,n] described in a 2D discrete space is derived from an analog image a(x,y) in a 2D continuous space through a sampling process that is frequently referred to as digitization.

Continuous Image Digitization

The 2D continuous image a(x,y) is divided into N rows and M columns. The intersection of a row and a column is termed a pixel. The value assigned to the integer coordinates [m,n] with {m=0,1,2,…,M–1} and {n=0,1,2,…,N–1} is a[m,n]. In fact, in most cases a(x,y) which we might consider to be the physical signal that impinges on the face of a 2D sensor is actually a function of many variables including depth (z), color , and time (t). In the above figure the coordinates with [m = 10, n = 3] has the highest brightest value.

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