Useful Applications For Digital Image example essay topic

Pg. 1 Introduction In the last thirty years digital image processing has grown from a scientific research field to a technical area that has many scientific and commercial applications. Many of these applications are due to the enormous improvements in computer technologies. Electronic image processing is now readily available on most typical desktop computers as opposed to the past when only a limited few had these capabilities. Digital image processing has been used to determine the brightness of the stars in a picture from a telescope, to determine the structure of a virus in a microscope image, and to produce highly accurate maps of the earth from satellite-gathered pictures. It has been used to control a sausage slicing machine to get equal weight slices from the irregularly shaped sausage, and to help restore classic paintings.

It has applications in medicine, cartography, industry, manufacturing, printing, and publishing, cosmetics and personal grooming, and a variety of scientific and research fields. In all the cases, digital image processing is concerned with the computer processing of pictures or, more generally, images that have been converted into numeric form. These images can come from many sources. Digital cameras that are broadly available produce digital images instead of the classic piece of exposed film, although any photograph can be digitized by using devices such as scanners and microdensitometer's. In fields such as medicine, materials testing, and astronomy, instruments are available that produce digital images from X-rays, gamma rays, and ultrasound waves. Satellite sensors directly produce digital images from Pg. 2 measurements of reflected or emitted visible, infrared, or microwave radiation.

There are many useful applications for digital image processing and the list could continue indefinitely. The main focus of this report is to provide the fundamentals of digital image processing. Also to enable the reader to know what digital image processing is and how it works, and also to provide useful examples of digital image processing. Digital Image Processing A natural image captured with a camera, telescope, microscope, or other type of optical instrument displays a continuously varying array of shades and color tones. Photographs made with film, or video images produced by a television camera tube, are a division of all possible images and contain a wide spectrum of intensities. They range from dark to light, and a spectrum of colors that can include just about any imaginable hue and saturation level.

Images of this type are referred to as continuous-tone because the various tonal shades and hues blend together without disruption to generate a faithful reproduction of the original scene. Continuous-tone images are produced by analog optical and electronic devices, which accurately record image data by several methods, such as a sequence of electrical signal fluctuations or changes in the chemical nature of a film emulsion that vary continuously over all dimensions of the image. In order for a continuous-tone or analog image to be processed or displayed by a computer, it must first be converted into a computer-readable form or digital format. This process applies to all Pg. 3 images, regardless the origin and complexity, and whether they exist as black and white (grayscale) or full color.

To convert a continuous-tone image into a digital format, the analog image is divided into individual brightness values through two operational processes that are termed sampling and quantization, as illustrated in Figure 1. The analog representation of a miniature young starfish imaged with optical microscope is presented in Figure 1 (a). After sampling in a two-dimensional array (Figure 1 (b) ), brightness levels at specific locations in the analog image are recorded and subsequently converted into integers during the process of quantization (Figure 1 (c) ). The target objective is to convert the image into an array of discrete points that each contain specific information about brightness or tonal range and can be described by a specific digital data value in a precise location. The sampling process measures the intensity at successive locations in the image and forms a two-dimensional array containing small rectangular blocks of intensity information.

After sampling is completed, the resulting data is quantized to assign a specific digital brightness value to each sampled data point, ranging from black, through Pg. 4 all of the intermediate gray levels, to white. The result is a numerical representation of the intensity, which is commonly referred to as a picture element or pixel, for each sampled data point in the array. The minimum value a pixel can have is typically 0, and the maximum depends on how the number is stored in the computer. Different formats allow different maximums. One way is to store each pixel as a single bit, which means it can take only the values 0 and 1, or black and white. Another common way is to store each pixel as a byte, which is 8 bits.

In this form the maximum pixel value is 255. Other formats are possible depending on the computer architecture. A table called the image histogram stores the value of each pixel in an image which can be utilized for modifying an image. Because images are generally square or rectangular in dimension, each pixel that results from image digitization is represented by a coordinate-pair with specific x and y values arranged in a typical Cartesian coordinate system.

The x coordinate specifies the horizontal position or column location of the pixel, while the y coordinate indicates the row number or vertical position. By convention, the pixel positioned at coordinates (0, 0) is located in the upper left-hand corner of the array, while a pixel located at (158,350) would be positioned where the 158th column and 350th row intersect. In many cases, the x location is referred to as the pixel number, and the y location is known as the line number. Therefore, a digital image is composed of a rectangular (or square) pixel array representing a series of intensity values and ordered through an organized (x, y) coordinate system. In reality, the image exists only as a large serial array of numbers (or data values) that can be interpreted by a computer to produce a digital representation of the original scene.

An image is digitized to convert it to a form which can be stored in a Pg. 5 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 image processing operations. Image processing operations can be roughly divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image.

Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using image enhancement techniques. Once the image is in good condition, the measurement extraction operations can be used to obtain useful information from the image. Some examples of image enhancement and measurement extraction are given below. The examples shown all operate on 256 grey-scale images. This means that each pixel in the image is stored as a number between 0 to 255, where 0 represents a black pixel and 255 represents a white pixel and values in-between represent shades of grey. These operations can be extended to operate on color images.

The following examples represent only a few of the many techniques available for operating on images. Pg. 6 Image Enhancement and Restoration The image at the left of Figure 2 has been corrupted by noise during the digitization process. The clean image at the right of Figure 2 was obtained by applying a median filter to the image. Figure 2. Application of the median filter Pg. 7 An image with poor contrast, such as the one at the left of Figure 3, can be improved by adjusting the image histogram to produce the image shown at the right of Figure 3.

Figure 3. Adjusting the image histogram to improve image contrast Pg. 8 Image Measurement Extraction Figure 4. Thresholding an image and applying a Watershed Separation Filter The example below demonstrates how one could go about extracting measurements from an image. The image at the top left of Figure 4 shows some objects. The aim is to extract information about the distribution of the sizes (visible areas) of the objects. The first step involves segmenting the image to separate the objects of interest from the background.

This usually involves thresholding the image, which is done by setting the values of pixels above a certain threshold value to white, and all the others to black (top right of Figure 4). Because the objects touch, thresholding at a level which includes the full surface of all the objects does not show separate objects. This problem is solved by performing a watershed separation on the image (lower left of Figure 4). The image at the lower right of Figure 4 shows the result of performing a logical AND of the two images at the left of Figure 4. This shows the effect that the watershed separation has on touching objects in the original image. Pg. 9 In general, the purpose of digital image processing is to enhance or improve the image in some way, or to extract information from it.

Typical operations are to: remove a blur from an image; smooth out the graininess, speckle, or noise in an image; improve the contrast or other visual properties of an image prior to displaying it; segment an image into regions such as object and background; magnify, minify, or rotate an image; remove wraps or distortions from an image; code the image in some efficient way for storage or transmission. There are other kinds of image processing such as optical and photographic and electrical analog. The first includes the use of lenses, enlargers, and the many photographic techniques such as dodging and un sharp masking to scale, vary colors, reduce blur and so on in pictures. The second kind covers standard television in which images are converted into electrical signals but not numbers as in digital image processing. Both of these fields can perform complex operations as in digital image processing, but digital image processing carries several advantages over other kinds of image processing techniques.

The first advantage is precision. In each creation of photographic process, there is a loss of image quality, and electrical signals are degraded by the physical limitations of the electrical components, whereas digital image processing can essentially maintain exact precision. The next advantage is its extreme flexibility. When using digital image processing numerous adjustments can be made to an image.

When using a digital image processing, an image can not only be magnified as with an ordinary enlarger, but one part can be magnified, another reduced, another rotated, and so Pg. 10 on. The main disadvantages of digital processing are its speed and expense. Although these disadvantages have been greatly reduced by the rapid increase in computer technology and the associated lower cost of the modern computer industry. Most digital image processing operations are now available on personal computers and desktop work stations.

Conclusion In today's times, technology is rapidly changing. In the last thirty years digital image processing has grown from a scientific research field to a technical area that has many scientific and commercial applications. Many of theses applications are due to the enormous improvements in computer technologies. Electronic image processing is now readily available on most typical desktop computers as opposed to past when only a limited few had these capabilities.

Many of the digital image capturing devices are becoming more available to the public than ever before. Most digital camera systems provide an easy means of transferring images to a standard desktop computer for processing and storage. Digital image processing is slowly, but surely replacing the old chemical processing techniques that the world once embraced. Due to this research paper one should have a fundamental understanding of what digital image processing is and how it works.

This research has not only uncovered scientific applications of digital image processing, but commercial and common applications as well. Just recently personal cell phones have been equipped with compact digital cameras capable of conveniently capturing digital images that can be processed by a personal computer. In Pg. 11 addition to the cell phone, several companies are manufacturing digital camcorders that are capable of digitally recording a video from the source! The future of digital image processing is truly out of site!