Digital image processing
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Abstract
A Few years ago, image processing was done largely in the analog domain, mainly by optical devices. These optical methods are still essential to applications such as holography because they are inherently parallel, however due to the significant increase in computer speed, these techniques are Increasingly being replaced by digital image processing methods. Digital image processing has become a vast domain for modern signal technologies. Hence its applications pass for simple aesthetical considerations and they include medical imagery, television and multimedia signals. It allows one to enhance image features of interest while attenuating detail irrelevant to a given application. And then extract useful information about the scene from the enhanced image. Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Digital image processing, either as enhancement for human observers or performing autonomous analysis, offers advantages in cost, speed, and flexibility, and with the rapidly falling price and rising performance of personal computers it has become the dominant method in use. An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image has been digitized, it can be operated upon by various images processing operations. Image processing operations can be roughly divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement extraction.
Introduction
Digital image processing is a broad field with extensive literature. This introduction could only summarize some of the more important methods in common use and may suffer from a bias towards industrial applications. One of the first applications of digital images was in the newspaper industry, when pictures were first sent by submarine cable between London and New York. Introduction of the Bartlane cable picture transmission system in the early 1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three hours. Specialized printing equipment coded pictures for cable transmission and then reconstructed them at the receiving end. Some of the initial problems in improving the visual quality of these early digital pictures were related to the selection of printing procedures and the distribution of intensity levels.
What is digital image processing?
An image may be defined as a two-dimensional function, f(x, y), 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. When (x, y) and amplitude 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. Note that a digital image is com- posed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image .Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, where limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image Processing encompasses a wide and varied field of applications. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to Emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate Human intelligence. There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum low-, mid-, and high-level processes. We see that a logical place of overlap between image processing and image analysis is the area of recognition of individual Regions or objects in an image. As will become evident shortly, digital image processing, as we have defined it, is used success- fully in a broad range of areas of exceptional social and economic value.
Explaining digital image processing
The term digital image processing generally refers to processing of a two-dimensional picture by a digital computer. In a broader context, it implies digital processing of any two-dimensional data. A digital mage is an array of real or complex numbers .Digital image processing has a broad spectrum of applications, such as remote sensing via satellites and other spacecrafts, image transmission and storage for business Applications medical processing, radar, sonar, and acoustic image processing, robotics, and automated industrial parts.
Interest in digital image processing methods stems from two principal application areas: Improvement of pictorial information for human interpretation; and processing of image data for storage, Transmission and representation for autonomous machine perception.
IMAGE REPRESENTATION AND MODELLING
In image representation one is concerned with characteristics of the quantity that each picture -Element (also called pixel) represents. An image could represent luminances of objects in a scene,The absorption characteristics of the body tissue (x-ray imaging) , the radar cross section of a target (rador Imaging), the temperature profile of a region (Infrared imaging) , or the gravitational field in an area ( in Geophysical imaging). An important consideration in image representation is the fidelity or the Intelligibility criteria for measuring the quality of an image or the performance of a processing technique.
Conclusion:
Finally we conclude that digital image processing is a rapidly evolving field with growing applications in science and engineering. This technology holds the possibility of developing the ultimate machine in the future that would be able to perform the visual functions. Many theoretical as well as technological breakthroughs are required before we could build such a machine. In the mean time, there is abundance of image processing applications that can serve mankind with the available and anticipated technology in the near future. Today, Image processing is considered to be one of the most rapidly evolving areas of information technology. It has growing applications in all areas of business. It also forms a core area of research within the computer science and engineering disciplines at most of the top universities and institutes in the US and other developed countries. Digital image processing has a become a vast domain for modern signal technologies.