MINDNODE MERGE 2 MAPS MANUAL
MINDNODE MERGE 2 MAPS FULL

I've noticed strange differences in work flow sometimes they are due to the release levels, sometimes not.

I have been listening through some of the video tutorials which are very good in themselves but sometimes they aren't %100 applicable to what I am looking to do, and other times they just bring up more questions. Sometimes terms and concepts are easy for the writer to understand but not to someone who is unfamiliar with the basic techniques and how they might influence the linear flow.

Vincent: That could be helpful, yet on the other hand it might be hard to communicate well.

MINDNODE MERGE 2 MAPS MANUAL

The scale differences are calculated using the homographies (projective transformations) between the frames.Ī proper manual is in the works, unfortunately I have no ETA myself. APP calculates the star size/shape and star density relative to the scale differences between the frames. Scale Independent quality parameters in APPĪPP has the parameters star density & relative FWHM which are very helpfull if you combine data of different scales. You will need to register both integrations first, before loading them into the RGB Combine tool.

MINDNODE MERGE 2 MAPS FULL

Last option, you can make 2 integrations of the two datasets and combine them using the RGB Combine tool in which you have full control over how the images are combined.

This is a very nice integration setting if you have some frames without perfect guiding but still want those frames to help reduce the noise in the data. The smaller and rounder the stars are, the higher the weight of that frame. Star shape means both roundness and size. Frames that have stars that are NOT round are punished a lot, so these frames will have little weight. This will give you the integration with the sharpest results.

integrate with weights: star shape, if you want the frames with the smallest and roundest stars to have the most weight.

integrate with weights: star density, if you want to give more weight to the frames with the highest star density, this usually is a very good parameter to indicate good transparancy.

integrate with weights: noise, if you aim to have the lowest noise in the end result.

Also bad frames with some clouds or bad transparancy will give higher SNR strongly reducing the integration result. I wouldn't advise to use SNR for weights. From all the settings, this is the least attractive one. Any deviating gradients between the frames will make the SNR metric totally unreliable.

integrate with weights: SNR, if you want to use the SNR of the frames.Usually this gives the best integration for noise and sharpness combined. The quality parameter is based on noise, stardensity, star size and shape. integrate with weights: quality, if you want to use APP's quality calculation.The integration should have better SNR but less sharpness integrate with weights: exposure, if you want the longer exposure frames, which should have higher SNR, to have more weight.integrate with weights: equal, if you want all frames to be weighted equal, this will give you a result which will be subtoptimal in SNR, and suboptimal in sharpness.You can play with the weights in 6) INTEGRATE Then proceed with your calibrated frames.

If you have RGB data with chromatic aberration save them with the 'align channels' mode, which reduces the amount of chromatic aberration significantly in most cases. If you still need to calibrate the data, then I would advise to calibrate both sets of data and save them in 2). There are several ways that you can do this and it all depends on what you are trying to achieve.

Mindnode merge 2 maps