Difference between revisions of "Using MIPAV for astronomical image processing"
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| − | If we look at image processing from the mathematical perspective, all digital images (no matter how they were acquired) are just arrays of numbers that can be manipulated by image processing software. | + | If we look at image processing from the mathematical perspective, all digital images (no matter how they were acquired) are just arrays of numbers that can be manipulated by image processing software. Across different fields of study, image processing applications, however developed for very specific needs, often use similar routines based on common image processing algorithms. |
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| − | Across different fields of study, image processing applications, however developed for very specific needs, often use similar routines based on common image processing algorithms. | + | |
| + | == Introduction == | ||
MIPAV is a very flexible image-processing package. It is platform-independent and free. Although it was intended for medical image processing, it also supports various non-medical imaging formats including FITS, which is used in astronomy. The software has a particular emphasis on image analysis and offers a number of tools for data reduction and image calculation, which can be used in deep sky and planetary imaging. | MIPAV is a very flexible image-processing package. It is platform-independent and free. Although it was intended for medical image processing, it also supports various non-medical imaging formats including FITS, which is used in astronomy. The software has a particular emphasis on image analysis and offers a number of tools for data reduction and image calculation, which can be used in deep sky and planetary imaging. | ||
MIPAV has a well-developed “plugin” interface, that allows users to write their own plug-ins. MIPAV has a very active user community that already contributed many freely available plugins, some of which are very useful for astronomy. | MIPAV has a well-developed “plugin” interface, that allows users to write their own plug-ins. MIPAV has a very active user community that already contributed many freely available plugins, some of which are very useful for astronomy. | ||
| + | === Why astronomy? === | ||
The following features makes MIPAV a good choice for use in astronomy: | The following features makes MIPAV a good choice for use in astronomy: | ||
| − | + | * It supports both FITS and raw formats, as well as many others including TIFF, JPEG, etc. | |
| − | + | * It was an extensive used documentation in a form of public wiki (I support it- http://mipav.cit.nih.gov/pubwiki/index.php/MIPAV_Help) including instructions for beginners, advanced used s and tutorials. | |
| − | + | * It is free. | |
| − | + | * One can write one’s own plug-ins. | |
| − | + | * MIPAV algorithms are more mathematically “transparent” than those used in commercial packages such as Adobe Photoshop and MaxIm DL. | |
| − | + | * MIPAV keeps the image provenance information in the .xmp files (which is an XML format file). The .xmpl file has the same name as the image file and it keeps a log of all transformations performed on the image, including time, date, algorithms used and parameters applied and even transformation matrices. I found this very helpful. | |
| − | + | ||
| + | == Topic outline == | ||
This topic will outline the technique that can be used for processing galaxy images. MIPAV algorithms are used for both astronomical data reduction and for making “pretty pictures”. Two case studies are presented. | This topic will outline the technique that can be used for processing galaxy images. MIPAV algorithms are used for both astronomical data reduction and for making “pretty pictures”. Two case studies are presented. | ||
Revision as of 01:17, 3 May 2013
If we look at image processing from the mathematical perspective, all digital images (no matter how they were acquired) are just arrays of numbers that can be manipulated by image processing software. Across different fields of study, image processing applications, however developed for very specific needs, often use similar routines based on common image processing algorithms.
Introduction
MIPAV is a very flexible image-processing package. It is platform-independent and free. Although it was intended for medical image processing, it also supports various non-medical imaging formats including FITS, which is used in astronomy. The software has a particular emphasis on image analysis and offers a number of tools for data reduction and image calculation, which can be used in deep sky and planetary imaging. MIPAV has a well-developed “plugin” interface, that allows users to write their own plug-ins. MIPAV has a very active user community that already contributed many freely available plugins, some of which are very useful for astronomy.
Why astronomy?
The following features makes MIPAV a good choice for use in astronomy:
- It supports both FITS and raw formats, as well as many others including TIFF, JPEG, etc.
- It was an extensive used documentation in a form of public wiki (I support it- http://mipav.cit.nih.gov/pubwiki/index.php/MIPAV_Help) including instructions for beginners, advanced used s and tutorials.
- It is free.
- One can write one’s own plug-ins.
- MIPAV algorithms are more mathematically “transparent” than those used in commercial packages such as Adobe Photoshop and MaxIm DL.
- MIPAV keeps the image provenance information in the .xmp files (which is an XML format file). The .xmpl file has the same name as the image file and it keeps a log of all transformations performed on the image, including time, date, algorithms used and parameters applied and even transformation matrices. I found this very helpful.
Topic outline
This topic will outline the technique that can be used for processing galaxy images. MIPAV algorithms are used for both astronomical data reduction and for making “pretty pictures”. Two case studies are presented.
Case study 1 shows how to use MIPAV to reduce the galaxy photometry data from the given sample of multi-band image of M63 – the Sunflower Galaxy from the McDonald Observatory. The raw images were combined into 3-color images of M63. The image processing includes noise reduction, cropping, scaling, alignment, and creating a single multi-color image of a given galaxy. The result is compared wither result received from processing the same set of data in MaxImDL.
Case study 2 - TBD.
