The History Of Image Processing

Published Date: 02 Nov 2017

Abstract

The Field of image processing is continually evolving. During the past five years, there has been a significant increase in the level of interest in image morphology, neural networks, full-color image processing, image data compression, image recognition, and knowledge-based image analysis systems.

Image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation, and processing of scene data for autonomous machine perception.

Image is better than any other information form for our human being to perceive. Vision allows humans to perceive and understand the world surrounding us. Image understanding, image analysis, and computer vision aim to duplicate the effect of human vision by electronically (= digitally, in the present context) perceiving and understanding image(s)In digital image processing system, first step in the process is Image Acquisition it require acquiring an image, After a digital image has been obtained, the next step deals with Preprocessing its function is to improve the image in ways that increase the chance for success of the other processes, the next step deals with Segmentation it partitions an input image into its constituent parts or objects, Representation & Description deals with make data in the form that suitable for computer processing, and after that Recognition is that assigns a label to an object, and last Interpretation involves meaning to an assemble of recognized objects.

In ordered to process an image it means to apply some operation on image. There are basically image enhancement means improvement in appearance of image, image restoration means to restore an image as it is after apply any operation, image compression to reduce the amount of data of an image to reduce size.

At last, in my paper I have covered the application of image processing in different areas like industrial, forensic fields etc.

Topics Covered

What is Image Processing?

Need of Image Processing.

General Digital Image Processing System.

Elements of Image Processing.

Operations on Image.

Application of image processing.

What is Image Processing?

Digital image processing can seem like a daunting subject for many people, but there are really only a few principles you need to know to use most graphics applications. I cover basic information about the way your computer sees images, and tips and tricks to get the most out of your images. Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. W e will focus on the fundamental concepts of image processing like when we are consider scanner it is a device which is used to scan images but at some time an image which we want to scan it is not properly scan at that time image processing is there to solve your problem. Space does not permit us to make more than a few introductory remarks about image processing in detail.

Why we need digital image processing?

Image is better than any other information form for our human being to perceive. Vision allows humans to perceive and understand the world surrounding us.

Image understanding, image analysis, and computer vision aim to duplicate the effect of human vision by electronically (= digitally, in the present context) perceiving and understanding image(s).

Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation, and processing of scene data for autonomous machine perception.

As per example, when we scan a picture through scanner it is not exactly as what actually we want, and whatever task which is we are going to perform using that image we do not achieve correctly, so that’s why image processing comes into picture.

As we know satellite is the more useful tool to get information about universe as well as earth, many of the decisions take place which are not taken directly with assumption are taken by us using images which are provided by satellite but images which are taken by satellite are in the form of RGB combination and hence we have to convert it into its appropriate color combination as well as its proper format at that time image processing is done. Satellites send these images or any data as digital signals and that is processed by computers.

Now I will describe you basic elements of image processing, operations which performs on images and softwares that provides image processing environment.

General Purpose Digital Image Processing System

Digital image processing encompasses a broad range of hardware, software, and theoretical underpinnings. Figure below shows that the overall object is to produce a result from a problem domain by means of image processing.

Knowledge Base

Preprocessing

Image Acquisition

Recognition and Interpretation

Segmentation

Representation & description

Problem Domain

Result

General purpose digital processing system

The first step in the process is image acquisition – that is, to acquire a digital image. To do so requires an imaging sensor and the capability to digitize the signal produced by the sensor. The sensor could be a monochrome, color TV or line scan camera. If the output of the camera other imaging sensor is not already in

digital form, an analog to digital converter digitizes it. The nature of the sensor and the image it produces are determined by the application.

After a digital image has been obtained, the next step deals with Preprocessing that image. The key function of

preprocessing is to improve the image in ways

that increase the chances for success of the other processes. Preprocessing typically deals with techniques for enhancing contrast, removing noise, and isolating regions whose textures indicate a likelihood of alphanumeric information.

The next stage deals with Segmentation. It partitions an input image into its constituents parts or objects. A rugged segmentation procedure brings the process a long way toward successful solution of an imaging problem. Weak or erratic segmentation algorithms almost always guarantee eventual failure, in terms of character recognition, the key role of segmentation is to extract individual characters and words from the background.

A Representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting features that result in some quantitative information of interest or features that are basic for differentiating one class of objects from another. In terms of character recognition, descriptors such as lakes (holes) and bays are powerful features that help differentiate one part of the alphabet from another.

Recognition is the process that assigns a label to an object based on the information provided by its descriptors. Interpretation involves assigning meaning to an ensemble of recognized objects. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. The use of double-headed arrows linking the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules. This depiction indicates that communication between processing modules generally is based on prior knowledge of what a result should be.

Elements of Image Processing

Image Acquisition

Two elements are required to acquire digital images. The first is a physical device that is sensitive to a band in the electromagnetic energy spectrum (such as x-ray, ultraviolet, visible, or infrared bands) and that produces an electrical signal output proportional to the level of energy sensed. The second called a digitizer, is a device for converting the electrical output of the physical sensing device into digital form.

Communication As an example, consider the basics of x-ray imaging systems. The output of an x-ray source is directed at an object and a medium sensitive to x-rays is placed on the other side of the object. The medium thus acquires an image of materials (such as bones and tissue) having various degrees of x-ray absorption. The medium itself can be film, a television camera combined with a converter of x-rays to photons, or discrete detectors whose outputs are combined to reconstruct a digital image.

Basic functional elements of image processing system

Image acquisition

Display

Sensors and Digitizers (Video Camera &

Scanners)

Processing

Operator Console (TV monitors, film, printers)

Digital Computer

(Optical disk, mag. Disks)

Storage

Storage:

Providing adequate storage is usually a challenge in the design of image processing systems. Digital storage for image processing applications falls into three principal categories:

(1) Short term storage for use during processing: One method of providing short term storage is computer memory. Another is by specialized boards, called frame buffers, which store one or more images and can be accessed rapidly.

(2) On-line storage for relatively fast recall: On-line storage generally takes the form of magnetic disks. Winchester disks with hundreds of Mbytes are common. A more recent tech., called magneto-optical (MO) storage, uses a laser and specialized materials technologies.

(3) Archival storage, characterized by infrequent access: Archival storage is characterized by massive storage requirements, but infrequent need for access. Magnetic tapes and optical disks are the usual media for archival applications.

Processing:

Processing of digital images involves procedures that are usually expressed in algorithmic form. Most image processing functions can be implemented in software. The only reason for specialized image processing hardware is the need for speed in some applications or to overcome some fundamental computer limitations. The trend continues toward miniaturizing and merging general purpose small computers equipped with image processing hardware. The principal imaging hardware being added to these computers consists of a digitizer/frame buffer combination for image digitization and temporary storage, a so-called arithmetic/logic unit (ALU). When combined with other software for applications such as spread sheets and graphics, it provides an excellent starting point for the solution of specific image processing problems.

Communication:

Communication in digital image processing primarily involves local communication between image processing systems and remote communication from one point to another, typically in connection with the transmission of image data. Hardware and software for local communication are readily available for most computers. Communication across vast distances presents a more serious challenge if the intent is to communicate image data rather than abstracted results. Wireless links using intermediate stations, such as satellites, are much faster, but they also cost considerably more.

Display:

Monochrome and color TV monitors are the principal display devices used in modern image processing systems. Monitors are driven by the output(s) of a hardware image display module in the backplane of the host computer or as part of the hardware associated with an image processor. The signals at the output of the display module can also be fed into an image recording device that produces a hard copy (slides, photographs, or transparencies) of the image being viewed on the monitor screen. Other display media include random-access cathode ray tubes (CRTs), and printing devices.

Operations on Image

There are following operation which are performs on image

1. Image Transforms 2.Image Enhancement 3. Image Restoration 4.Image Compression 5.Image segmentation 6.Representation and Description 7.Recognition and Interpretation. But I am going to explain you three very important operations, which are followings.

Image Enhancement Using Filters

What is Image Enhancement? The term image enhancement we mean improvement of the appearance of an image by increasing dominance of some features or by decreasing ambiguity between different regions of the image. The principle objective of image enhancement techniques is to process an image so that the result is more suitable than the original image for a specific application. The word specific is important, because enhancement technique that is suitable for biomedical images can be a total catastrophe for remotely sensed images. The quality of an image depends on the purpose for which the images acquired or displayed.

Use of Filters in Image Enhancement: Filter as name suggest that they are some what related with to filter something means in an image enhancement techniques filters are used to give some special effects on the images, for example, image sharpening, blurring, contrast, smoothing etc.

Smoothing Filters Are used for blurring and for noise reduction. Blurring is used in preprocessing steps, such as removal of small details from an image prior to (large) Object extraction, and bridging of small gaps in lines or curves. Noise reduction can be accomplished by blurring with a linear filter and also by non-linear filtering.

Lowpass Filters: Linear filters are based on the concepts on spatial filtering which state that the transfer function and the impulse or point spread function of a linear system are inverse Fourier transforms of each other. So called lowpass filters attenuate or eliminate high-frequency components in the Fourier domain while leaving low frequencies untouched (that is, the filter "passes" low frequencies). High-frequency components characterize edges and other sharp details in an image, so the net effect of lowpass filtering is image blurring.

Median Filtering: One of the principle difficulties of the smoothing method is that it blurs edges and other sharp details. If the objective is noise reduction rather than blurring, an alternative approach is to use median filters. The gray level of each pixel is replaced by the median of the gray levels in neighborhood of that pixel, instead of by the average. This method is particularly effective when the noise pattern consist s of strong, spikelike components and the characteristics to be preserved is edge sharpness. Median filters are nonlinear.

Sharpening Filters: The principle objective of sharpening is to highlight fine detail in image or to enhance detail that has been blurred, either in error or as natural effect of particular method of image acquisition. Use of image sharpening vary and include applications ranging from electronic printing and medical imaging to industrial inspection and autonomous target detection in smart weapons.

Highpass filters: It attenuates or eliminates low-frequency components. Because these components are responsible for the slowly varying characteristics of an image, such as overall contrast and average intensity, the net result of sharpening of edges and other sharp details.

Bandass filtering: It removes selected frequency regions between low and high frequencies. These filters are used for image restoration and are seldom of interest in image enhancement.

Image Restoration

As in Image Enhancement, the ultimate goal of Image Restoration technique is to improve an image in some sense. For the purpose of differentiation, we consider restoration to be a process that attempts to reconstruct or recover an image that has been degraded by using some a priori knowledge of degradation phenomenon. Thus restoration techniques are oriented toward modeling the degradation and applying the inverse process in ordered to recover the original image. One of the major application areas of image processing tech. is improving the quality of recorded images. No imaging system gives images of perfect quality because of degradations caused by various reasons. So, the image to be restored for subsequent computer processing or human viewing. For example, contrast stretching is considered an enhancement technique because it is based primarily on the pleasing aspects it might present to the viewer, whereas removal of image blur by applying a deblurring function is considered a restoration technique.

Image Compression

In this era of digital, we consider that most of the fields are used images for presentation or any other purpose rather than large volume of textual information. But Users of digital image processing tech. usually have to handle a large volume of data. Storing image data for future use needs large storage space. Similarly transmitting image data in reasonable time needs wide channel capacity. To reduce these requirements the technique we use is called data compression. So, compression tech. represents (almost) same pictorial information in a more compact form by removing redundancies. In essence, Compression tech. represents image data using fewer bits than what is required for original image. Thus this class of techniques may also include feature extraction/selection procedures.

Image compression model

There are many tech. used for image compression. To explain image compression model we have combine all this methods to form practical image compression systems. As figure below shows, a compression system consists of two distinct structural blocks: an encoder and a decoder. We can also consider them as compressor and decompressor respectively. An input image is fed into the Encoder or compressor, which creates a set of symbols from the input data. After transmission over the channel, the encoded representation is fed into the decoder or decompressor, where a reconstructed output image is generated. If it is, the system is the error free or information preserving; if not, some level of distortion is present in the reconstructedimage

.

Image

Image

Channel

Source Decoder

Channel Decoder

Channel Encoder

Source Encoder

A general compression model

Both the encoder and decoder shown in fig. consist of two relatively independent functions or subblocks. The encoder is made up of a source encoder, which removes input redundancies, and a channel encoder, which increases the noise immunity of the source encoder’s output. As would be expected, the decoder includes a channel decoder followed by a source decoder. If the channel between the encoder and decoder is noise free, the channel encoder and decoder are omitted, and the general encoder and decoder become the source encoder and decoder, respectively.

Application of Image Processing:

Digital image processing and analysis techniques are used today in a variety of problems .Many application oriented image analyzers are available and are working satisfactorily in real environment. The following are a few major application areas:

Office automation: Optical character recognition; document processing; cursive script recognition; logo and icon recognition; identification of area on envelop; etc.

Industrial automation: automatic inspection system; non-destructive testing; automatic assembling; process related to VLSI manufacturing; PCB checking; robotics; oil and natural gas exploration; seismography; process control applications; etc.

Bio-medical: ECG,EEG,EMG analysis; cytological; histological and stereological applications; automated radiology and pathology; X-ray image analysis; mass screening of medical images such as chromosome slides for detection of various diseases, mammograms, cancer smears; CAT, MRI, PET, SPECT, USG and other tomographic images; routine screening of plant samples; 3-d reconstruction and analysis; etc.

Remote sensing: Natural resources survey management; estimation related to agriculture, hydrology, forestry ; registration of satellite images with terrain maps; monitoring traffic along roads; etc;

Information technology: facsimile image transmission; videotext; video-conferencing and videophones; etc.

Entertainment and consumer electronics: HDTV; multimedia and video-editing; etc.

Criminology: finger print identification; human face registration and matching; forensic investigation; etc.

Military applications: missile guidance and detection; target identification; navigation of pilot less vehicle; reconnaissance; and range finding; etc.

In addition to above mentioned areas, another important application of image processing techniques is improvement of quality or appearance of a given image.