Drizzle software

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The code is originally derived from MySQL. The copyright and license notices on this page only apply to the text on this page. The software was originally developed as a set of Fortran routines which were available both as stand-alone executables and as IRAF tasks in the Dither package.

The original drizzle software was developed as a collaborative effort by Andy Fruchter and Richard Hook. This set of code formed the basis for a more automated task for aligning images to produce distortion-corrected images; namely, the Python task PyDrizzle Section 5. Finally, all the steps required to identify cosmic-rays using the aligned images produced by PyDrizzle were automated in the Python task Multidrizzle Section 5.

There are many tasks which are available in the PyRAF dither package. Many of them represent the original interface to the drizzle codeset in IRAF and are no longer supported - though their functionality has been included in the new scripts which are used by PyDrizzle and Multidrizzle. It is important for the user to realize that use of the non-supported functions may result in inferior output products since the most current updates to the code and methodology are only implemented in the Python functions called by PyDrizzle and Multidrizzle.

But, if you make the drops of data smaller than the initial pixels , you might start to see some improvements in sharpness in the integration result. The possible sharpness increase is offset by an increase of noise in the integration result. Now, When can I use Drizzle to benefit from an increase in sharpness in the integration result?

For an earth based observer using exposure times of several seconds and longer, this usually means that drizzle can be beneficial if your imaging scale is larger than the atmospheric seeing. So wide-field images with focal lenghts less than mm will almost always benefit from drizzle if 2 and 3 are met. On the other hand, if you image with long focal lenghts with an image scale of 0.

Then drizzle makes no sense really, you will only get more noise in your final result and no real increase in sharpnes. This will gather the extra data needed for the possible increase in sharpness. So if each image is shifted with 0. Drizzle is a BIG!

To qoute the official Nasa MultiDrizzle Handbook v3. Drizzle frequently divides the power from a given input pixel between several output pixels. As a result, the noise in adjacent pixels will be correlated The correlation of adjacent pixels implies that a measurement of the noise in a drizzled image on the output pixel scale underestimates the noise on larger scales.

So this is what Drizzle does with noise. It's rarely mentioned in the astrophotography sphere on the internet fora but it's really important to realize. This results in sharper integrations possibly, but also in more noise as a consequence. Only the Bayer pixels are used in the drizzle integration. Hello Mabula, I recently had an article in an astro magazine about Drizzle read. There it was noted that an integration only with the average mode goes. Without outliere rejection, because the pixels of the finer grid are only operated alternately with content, which is similar to the occurrence of a disturbing pixel.

Those would be sorted out at median or sigma stacking. Would this also apply to Bayer Drizzle?