Author Topic: Hot Pixels not rejected in DrizzleIntegration  (Read 5938 times)

Offline mstriebeck

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Re: Hot Pixels not rejected in DrizzleIntegration
« Reply #15 on: 2016 November 04 08:49:48 »
Is this with the latest ImageIntegration process? Or did you also make updates to DrizzleIntegration?

I started last night to process with the latest ImageIntegration ... but fell asleep in the middle ... Too much work ... Will try again tonight.

Offline vicent_peris

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Re: Hot Pixels not rejected in DrizzleIntegration
« Reply #16 on: 2016 November 04 10:57:04 »
Is this with the latest ImageIntegration process? Or did you also make updates to DrizzleIntegration?

I started last night to process with the latest ImageIntegration ... but fell asleep in the middle ... Too much work ... Will try again tonight.


The ImageIntegration module was updated. This module has several tools, and the one updated was DrizzleIntegration.

Offline mstriebeck

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Re: Hot Pixels not rejected in DrizzleIntegration
« Reply #17 on: 2016 November 05 14:21:56 »
Hi Vicent,

I just tried out the Gaussian kernel function. And it did reduce my FWHM - I observed a reduction from 3.7 to 3.5. But the SNR was worse (the SNRWeight went from 6.9 to 2.8!) It's even visible in the (stretched) images. Or I'm still doing something wrong ...

Square Drops


Gaussian Drops

Offline Juan Conejero

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Re: Hot Pixels not rejected in DrizzleIntegration
« Reply #18 on: 2016 November 06 01:36:10 »
Hi Mark,

This is normal. For any given data set, the improvement in resolution always comes from a SNR reduction with drizzle. Drizzle performs a convolution with a more peaked, smaller PSF, when you use a Gaussian drop kernel. In SNR terms, the effect is similar to lowering the drop shrinking parameter: drops are smaller and hence transport less Bourbon, so the final integrated image is less happy :)

Since Gaussian and variable shape kernels already reduce the volume of each drizzle drop, you should set drop shrinking to 1 when you use them, unless you have a really huge data set. Whether using these kernels to achieve a resolution improvement is worth the associated SNR reduction is up to you, and depends on the data you have, on what you want to get, or what is more interesting for you. Experiment.
Juan Conejero
PixInsight Development Team
http://pixinsight.com/