An image may be defined as a two dimensional function, \( f(x,y) \), where \(x\) and \(y\) are spatial(plane) coordinates. 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 the intensity value of \(f \) all are finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of digital computer.
Digital image is composed of a finite number of elements, each of which has a particular location and a value. These elements are called pixels, picture elements, image elements or pels. Pixel is the term used most widely to denote the element of a digital image.
There is no clear cut boundaries in the continuum from image processing at one end to computer vision at the other end. However one useful paradigm is to consider three types of computerized process in this continuum: low, mid and higher-level processes.
Low-Level Processing involve primitive operation such as image processing to reduce noise, contrast enhancement and image sharpening. A low-level process is characterized by the fact that both the inputs and outputs are images.
Mid-Level Processing on images involves task such as segmentation (partitioning an image into regions or objects), descriptions of those objects to reduce them to a from suitable for computer processing and classification (recognition) of individual objects. A mid-level process is characterized by the fact that the inputs generally are images, but its outputs are attributes extracted from those images (e.g. edges, contours and the identity of the individual objects).
Higher-Level Processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.
Sources of the Digital Image
The areas of application of the digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest way to develop a basic understanding of the extent of image processing application is to categorize images according to their source (e.g. Visual, X-ray and so on). The principal energy source for images in used today is the electromagnetic energy spectrum. Other important source of energy include acoustic, ultrasonic, and electronic. Synthetic images, used for modeling and visualization, are generated by the computer.
Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can we thought of as a stream of mass-less particles, each travelling in a wavelike pattern and moving at the speed of light. Each mass-less particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown below ranging from gamma rays (highest energy) at one end to the radio waves (lowest energy) at the other.
The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.
Gamma-Ray Imaging: Major uses of imaging based on gamma rays include nuclear medicine and astronomical observation. In nuclear medicine, the approach is to inject a patient with a radioactive isotopes that emits gamma rays as it decays. Images are produced from the emissions collected by the gamma ray detector.
X-Ray Imaging: X-rays are among the oldest source of EM radiation used for imaging. The best known use of X-rays is medical diagnostics, but they are also used extensively in industry and other areas, like astronomy.
Imaging in the ultraviolet Band: Ultraviolet rays are used in applications such as lithography, microscopy, lasers, biomedical imaging and astronomical observations.
Imaging in the Visible and Infrared Bands: The infrared band is often used in conjunction with visual imaging. The major application area are light microscopy, astronomy, remote sensing, industry and law enforcement.
Imaging in the Microwave Band: Microwave are useful in radar and medical imaging, RADAR is an abbreviation of RAdio Detection And Ranging. Radar system use microwave in addition to radio waves at higher frequency and are used in application such as weather forecasting, imaging planetary surfaces and determining earth resources.
Imaging in the Radio Band: The major applications of imaging in the radio band are in medicine and astronomy. In medicine, radio waves are used in magnetic resonance imaging(MRI).
Examples in which other Imaging Modalities are Use: Although imaging in the electromagnetic spectrum is dominant by far, there are a number of other imaging modalities such as acoustic imaging, electron microscopy and synthetic imaging.
Fundamental Steps in Digital Image Processing
There are two categories of steps involved in the image processing. Methods whose input and output are images. e.g. Image Acquisition, Image Enhancement, Image Restoration, Color Image Processing, Wavelets and Multi-resolution processing, Compression. Methods whose input may image but whose output are attributes extracted from those images. e.g. Morphological Processing, Segmentation, Representation and Description, Recognition.
Image enhancement is the process of manipulating an image so that the result is more suitable than the original for a specific application. The word “specific” is important here, because it establishes at the outset that enhancement techniques are problem oriented. There is no general theory of image enhancement. When an image is processed for visual interpretation, the viewer is the ultimate judge of how well a particular method works, Therefore image enhancement is subjective process.
Image restoration is an area that also deals with improving the appearance of an image. However unlike enhancement it is an objective process. It is objective in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation.
Color Image Processing
Color image processing is an area that has been gaining importance because of the significant increase in the use of digital images over the internet.
Wavelets and Multi-resolution processing
Wavelets are the foundation of representing images in various degree of resolution. It is used in image data compression and for pyramidal representation, in which images are subdivided successively into smaller region.
Compression as the name implies deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.
Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.
Segmentation procedures partitioned an image into its constituent parts or object. In general, autonomous segmentation is one of the most difficult task in digital image processing.
Representation and Description
Representation and description almost always follow the output of segmentation stage, which usually a raw pixel data. Converting the raw data to a form suitable for computer processing is necessary. The first decision that should be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties such as texture or skeleton shape.
Description, also called feature selection, deals with extracting attributes that result in some qualitative information of interest or are basic for differentiating one class of object from another.
Recognition is the process that assigns a label to an object based on its descriptors.
Components of Image Processing System
Below figure shows the basic component comprising a typical general-purpose system used for digital image processing. Different components of an image processing system and their functionality are as follows:
The image sensor captures incoming light convert it into an electric signal. Image sensors is a 2D array of light-sensitive elements that convert photon to electron. CCD and CMOS image sensors are widely used in image-capturing devices like digital camera and camcorders.
Special Image Processing Hardware:
It consists of the digitizer and the specialize hardware that perform other primitive operations such as an arithmetic logic unit (ALU), which performs arithmetic and logical operations in parallel on entire image.
The computer in an image processing system is a general purpose computer and can range from a PC to Super Computer.
Image Processing Software:
Software for image processing consists of specialized module that perform specific tasks. A well designed packaged also includes the capability for the user to write code.
Mass storage capability is a must in image processing applications. Digital storage for image processing applications fall into three categories:
- Short term storage is use during image processing. Besides the computer memory, specialized board called frame buffer is also used. frame buffer store one or more images and can be accessed rapidly, usually at video rates(30 complete image/second).
- On-line Storage is used for relatively fast recall. Magnetic disk or optical-media storage is used for on-line storage.
- Archival Storage is characterized by infrequent use. Magnetic tapes and optical disks housed in jukeboxes are the usual media for archival application.
Image display in use today are mainly colored TV monitors. Monitors are driven by the outputs of image and graphics display card that are integral part of the computer system.
Hard-copy devices for recording images include laser printers, film cameras, heat-sensitive devices, inkjet-units and digital units such as optical and CD-ROM disks.
Networking is almost a default function of any computer system in use today. Because of the large amount of data inherent in image processing applications, the key consideration in image transmission is bandwidth.
This article is contributed by Ram Kripal. If you like eLgo Academy and would like to contribute, you can mail your article to firstname.lastname@example.org. See your article appearing on the eLgo Academy page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.