Image processing: Difference between revisions
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In [[imaging science]], '''image processing''' is any form of [[signal processing]] for which the input is an image, such as a [[photograph]] or [[video frame]]; the [[output]] of image processing may be either an image or a set of characteristics or [[parameter]]s related to the image. Most image-processing techniques involve treating the image as a [[two-dimensional]] [[signal (electrical engineering)|signal]] and applying standard signal-processing techniques to it. |
In [[imaging science]], '''image processing''' is any form of [[signal processing]] for which the input is an image, such as a [[photograph]] or [[video frame]]; the [[output]] of image processing may be either an image or a set of characteristics or [[parameter]]s related to the image. Most image-processing techniques involve treating the image as a [[two-dimensional]] [[signal (electrical engineering)|signal]] and applying standard signal-processing techniques to it. |
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Image processing usually refers to [[digital image processing]], but [[Optical engineering|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 science|imaging]]. |
Image processing usually refers to [[digital image processing]], but [[Optical engineering|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 science|imaging]].<br /> |
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'''Image Processing'''<br /> |
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An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).<br /> |
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In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.<br /> |
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Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories: * Image Processing image in -> image out * Image Analysis image in -> measurements out * Image Understanding image in -> high-level description out Image processing is referred to processing of a 2D picture by a computer. Basic definitions:<br /> |
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An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).<br /> |
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An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition. Sequence of image processing:<br /> |
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The most requirements for image processing of images is that the images be available in digitized form, that is, arrays of finite length binary words. For digitization, the given Image is sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is processed by a computer. To display a digital image, it is first converted into analog signal, which is scanned onto a display. |
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<br /> |
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| ⚫ | 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).<br /> |
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| ⚫ | 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. <br /> |
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Before going to processing an image, it is converted into a digital form. Digitization includes sampling of image and quantization of sampled values. |
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After converting the image into bit information, processing is performed. |
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This processing technique may be, Image enhancement, Image reconstruction, and Image compression |
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<br /> |
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''Image enhancement:''<br /> |
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It refers to accentuation, or sharpening, of image features such as boundaries, or contrast to make a graphic display more useful for display & analysis. This process does not increase the inherent information content in data. |
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It includes gray level & contrast manipulation, noise reduction, edge crispening and sharpening, filtering, interpolation and magnification, pseudo coloring, and so on. |
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<br /> |
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''Image restoration:''<br /> |
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It is concerned with filtering the observed image to minimize the effect of degradations. Effectiveness of image restoration depends on the extent and accuracy of the knowledge of degradation process as well as on filter design. Image restoration differs from image enhancement in that the latter is concerned with more extraction or accentuation of image features.<br /> |
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''Image compression:''<br /> |
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It is concerned with minimizing the no of bits required to represent an image. Application of compression are in broadcast TV, remote sensing via satellite, military communication via aircraft, radar, teleconferencing, facsimile transmission, for educational & business documents , medical images that arise in computer tomography, magnetic resonance imaging and digital radiology, motion , pictures ,satellite images, weather maps, geological surveys and so on.<br /> |
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Text compression – CCITT GROUP3 & GROUP4<br /> |
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Still image compression – JPEG<br /> |
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Video image compression -MPEG<br /> |
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| ⚫ | 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). |
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| ⚫ | 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. |
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==See also== |
==See also== |
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<ref>{{cite web|last=Ian|first=Young|title=Fundamentals of Image processing|url=http://www.tnw.tudelft.nl/fileadmin/Faculteit/TNW/Over_de_faculteit/Afdelingen/Imaging_Science_and_Technology/Research/Research_Groups/Quantitative_Imaging/Publications/List_Publications/doc/FIP2.2.pdf|work=Image Processing|accessdate=1 October 2012}}</ref> |
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<ref>{{cite web|title=Image Processing and Compression Techniques|url=http://www.101seminartopics.com/wp-content/uploads/2009/07/87IMAGE-PROCESSING-COMPRESSION-TECHNIQUES.pdf|work=Image Processing|accessdate=4 October 2012}}</ref> |
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==External links== |
==External links== |
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*[http://intopii.com/into/ Open source cross-platform image processing framework in C++ with JavaScript support] |
*[http://intopii.com/into/ Open source cross-platform image processing framework in C++ with JavaScript support] |
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<ref>{{cite book|last=Russ|first=J.C.|title=The Image Processing Handbook|year=1995|publisher=CRC Press|location=Boca Raton, FL|edition=2}}</ref> |
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[[Category:Image processing| *]] |
[[Category:Image processing| *]] |
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Revision as of 22:28, 4 October 2012
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In imaging science, image processing is any form of signal processing for which the input is an image, 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. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
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.
Image Processing
An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).
In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.
Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories: * Image Processing image in -> image out * Image Analysis image in -> measurements out * Image Understanding image in -> high-level description out Image processing is referred to processing of a 2D picture by a computer. Basic definitions:
An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).
An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition. Sequence of image processing:
The most requirements for image processing of images is that the images be available in digitized form, that is, arrays of finite length binary words. For digitization, the given Image is sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is processed by a computer. To display a digital image, it is first converted into analog signal, which is scanned onto a display.
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.
Before going to processing an image, it is converted into a digital form. Digitization includes sampling of image and quantization of sampled values.
After converting the image into bit information, processing is performed.
This processing technique may be, Image enhancement, Image reconstruction, and Image compression
Image enhancement:
It refers to accentuation, or sharpening, of image features such as boundaries, or contrast to make a graphic display more useful for display & analysis. This process does not increase the inherent information content in data.
It includes gray level & contrast manipulation, noise reduction, edge crispening and sharpening, filtering, interpolation and magnification, pseudo coloring, and so on.
Image restoration:
It is concerned with filtering the observed image to minimize the effect of degradations. Effectiveness of image restoration depends on the extent and accuracy of the knowledge of degradation process as well as on filter design. Image restoration differs from image enhancement in that the latter is concerned with more extraction or accentuation of image features.
Image compression:
It is concerned with minimizing the no of bits required to represent an image. Application of compression are in broadcast TV, remote sensing via satellite, military communication via aircraft, radar, teleconferencing, facsimile transmission, for educational & business documents , medical images that arise in computer tomography, magnetic resonance imaging and digital radiology, motion , pictures ,satellite images, weather maps, geological surveys and so on.
Text compression – CCITT GROUP3 & GROUP4
Still image compression – JPEG
Video image compression -MPEG
See also
Further reading
- Tinku Acharya and Ajoy K. Ray (2006). Image Processing - Principles and Applications. Wiley InterScience.
- Wilhelm Burger and Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer. ISBN 1-84628-379-5 and ISBN 3-540-30940-3.
{{cite book}}: Check|isbn=value: invalid character (help) - R. Fisher, K (2002). Springer. ISBN 3-540-67754-2.
{{cite book}}: Missing or empty|title=(help) - 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.
External links
- Lectures on Image Processing, by Alan Peters. Vanderbilt University. Updated 15 September 2011.
- EURASIP Journal on Image and Video Processing — Open Access journal on Image Processing
- Image Processing On Line – Open access journal with image processing algorithms, open source implementations and demonstrations
- Computer Vision Online A good source for source codes, software packages, datasets, etc. related to image processing
- Image processing is fun! A mathematical image processing blog
- IMMI - Rapidminer Image Mining Extension - tool for image processing and image mining
- Population open-source image processing library in C++
- IPRG Open group related to image processing research resources
- Image Tools .NET/MONO Batch image processing tool
- Open source cross-platform image processing framework in C++ with JavaScript support
- ^ Ian, Young. "Fundamentals of Image processing" (PDF). Image Processing. Retrieved 1 October 2012.
- ^ "Image Processing and Compression Techniques" (PDF). Image Processing. Retrieved 4 October 2012.
- ^ Russ, J.C. (1995). The Image Processing Handbook (2 ed.). Boca Raton, FL: CRC Press.