Image processing: Difference between revisions
Line 6: | Line 6: | ||
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of [[scientific visualization]] (of often large-scale complex scientific/experimental data). Examples include [[microarray]] data in genetic research, or real-time multi-asset portfolio trading in finance. |
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of [[scientific visualization]] (of often large-scale complex scientific/experimental data). Examples include [[microarray]] data in genetic research, or real-time multi-asset portfolio trading in finance. |
||
== Components of an Image Processing System == |
|||
In the 1980s, various techniques of image processing frameworks were put on the market throughout the world. Between the years of 1980 and 1990, the market moved to image processing equipment, as single panels intended to be better matched with commerce typical business and suitable for the PCs in technical work places. |
|||
Digital image processing techniques were developed in the 1960’s by Bell laboratories. The process uses computer algorithms to perform image processing on digital images. Digital image processing allows a much wider range of algorithms for image editing and can avoid problems such as build-up of noise and signal distortion. |
|||
Historically, there have been systems ranging from personal computers to super computers that were used for image processing. Currently, the trends of innovation are going towards scaling down and blending of general purpose small computers with specialized image processing hardware. The image processing methods are chiefly related to attainment of image, improvement in the image, the segmentation of image, the extraction of the features of the image, categorization of images, and many more. |
|||
The fundamental way to define digital image processing is related to the processing of image digitally. This includes the eradication of noise and indiscretions that exist in the image when using a digital PC. The noise and eradication causes creeping in image. |
|||
The digital image processing usually related to the processing of 2-D image with the help of a digital PC. The digital picture is the array of numbers characterized by fixed numeral of bits. A typical approach of storing an image digitally on computer by sampling the image at a rectangular grid. The color or intensity at each of these points is converted into a numeric value. In order to produce a digital image, two elements are required. The first is a physical device that is sensitive to the energy radiated by the object. The second is a ‘Digitizer’, which is a device for converting the physical device output into digital form. For example, the sensor of digital video camera produces an electrical output proportional to light intensity. |
|||
Programming for image processing consists of specialized modules that perform specific tasks. A much-planned bundle likewise incorporates capacity for a client to compose code by at least one computer language. |
|||
Mass storage capacity is a significant thing in image processing. A 1024x1024 pixels size of image, in which intensity of each pixel is an 8-bit quantity, requires one megabyte of storage space if the image is not compresses. Digital capacity for image processing applications are classified by three main ways. |
|||
1. Short duration store for use during image processing |
|||
2. Online store for quick review |
|||
3. Archival storage for occasional access |
|||
Image displays in use today are color monitors, which are driven by graphic display card that are an integral part of the computer system. The system network is just a default work in any PC framework being used today. As a result of the huge number of information inalienable in picture preparing applications, the main thought in picture transmission is transfer speed. In devoted systems, this ordinarily is not an issue. However, interchanges with remote locales by means of the Internet are not generally as productive. Luckily, this circumstance is enhancing rapidly because of optical fiber and other broadband innovations.<ref name="GonzalezWoods2008" /> |
|||
==See also== |
==See also== |
Revision as of 00:57, 4 May 2017
In imaging science, image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image.[1] Most image-processing techniques involve isolating the individual color planes of an image and treating them as two-dimensional signal and applying standard signal-processing techniques to them. Images are also processed as three-dimensional signals with the third-dimension being time or the z-axis.
Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.[2]
Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance.
See also
- Image analysis
- Image sharpening
- Image smoothing
- Multidimensional systems
- Near sets
- Photo manipulation
- Image compression
- digital imaging
- digital image processing
References
- ^ Rafael C. Gonzalez; Richard E. Woods (2008). Digital Image Processing. Prentice Hall. pp. 1–3. ISBN 978-0-13-168728-8.
- ^ Joseph P. Hornak, Encyclopedia of Imaging Science and Technology (John Wiley & Sons, 2002) ISBN 9780471332763
Further reading
- Tinku Acharya and Ajoy K. Ray (2006). Image Processing - Principles and Applications. Wiley InterScience.
- Wilhelm Burger and Mark J. Burge (2008). Digital Image Processing: An Algorithmic Approach Using Java. Springer-Verlag. ISBN 978-1-84628-968-2.
- Bernd Jähne (2002). Digital Image Processing (PDF). Springer. ISBN 3-540-67754-2.
- Tim Morris (2004). Computer Vision and Image Processing. Palgrave Macmillan. ISBN 0-333-99451-5.
- Tony F. Chan and Jackie (Jianhong) Shen (2005). Image Processing and Analysis - Variational, PDE, Wavelet, and Stochastic Methods. Society of Industrial and Applied Mathematics. ISBN 0-89871-589-X.
- Milan Sonka, Vaclav Hlavac and Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. ISBN 0-534-95393-X.
- John Russ (2011). The Image Processing Handbook. CRC Press. ISBN 9781439840450.
External links
- Lectures on Image Processing, by Alan Peters. Vanderbilt University. Updated 7 January 2016.
- Image Processing On Line – Open access journal with image processing algorithms, open source implementations and demonstrations
- IPRG Open group related to image processing research resources
- Bare Images Toolbox Online image processing and analysis application