There is some speckling in the lower right that we must despeckle. by 2 pixels and do the Value propagate again. Let's shrink the selection by Select->Shrink. Reduce the stars by Filters->Distorts->Value propagate. Deselect the mask and select the gradient-free M31 zoom in to see the selection: Adjust the slider enough to exclude speckles at the center: This will be the basis for our star mask that we create using Colors->Threshold. Subtract this from the layer that we created them from (the gradient free M31) and save this as a "new layer from visible": The stars can be made to opo out better by first reducing the image with the main low frequency behavior, by decomposing this image with wavelets. Next, to reduce the stars, we need an effective way to apply Threshold to define a mask. To be continued in the next post because of the attachment limit of 5 files. Now subtract it from the original (and save it as a "new layer from visible"): This is already a pretty good gradient but the edge is a bit sharp. Deselecting everything except the error we get: For this we deselect the nebulosity and fill it with black:ĭecompose this image using wavelets (Filters->Enhance->Wavelet-decompose.) and select scale 7. The initial image is a bit rough, stacked and edited in DSS, size reduced in Paint but it serves the purpose:įirst we use wavelets to estimate the gradient. I illustrate the workflow with an image from an Astrocat51 on an AVX, 60 times 2 minute exposures. By "low frequency" I mean blurry blobs representing the over all luminosity that can be used to capture the gradient, and also allows for a better use of Threshold and Erode. Wavelets to the rescue! The reason for this is that the error layer along with perhaps base layers 6 and 7, are good approximations for the "low frequency" parts. So how do we select those areas easily for the numerous stars? A stright forward Threshold will not work due to gradients and nebulosity. I recently found that by reducing the selection area to just around the stars, the results are much better. After one or two iterations the whole image shows rectangular-like blobs, which is more or less unacceptable. Secondly, why can't we use Erode or Value propagation for resucing the stars? I tried that in the most straightforward way without making a selection. Forging a plugin that calls Scilab, or rewriting this in Gimp's Python 2.7 without numpy, are both difficult to accomplish (allowing an external Anaconda release v 2 or 3 would help!) The gradient estimation is done on the non-black area so there is a useful selectivity. ![]() This works much better and gets me the right answer in one shot. jpg file, chops up the data in chunks based on the number of data points per polynomial parameter, then estimates a polynomial from Chebyshev base polynomials. Thus, it requires a lot of work with non-optimal results. As a result one has to try multiple gradient removals in different directions, which has the side effect of creating new gradients. Many (most) gradients do not satisfy that model. I have used it quite a bit but it is specialized for gradients that are parallel perpendicular to the selected gradient removal line, and this is limiting. I should first mention why the standard gradient removal tool does not work well for this. My first take on this was to write a plugin for gradient removal using Scilab but yesterday I found a quicker way just using native Gimp features that I want to share. Or perhaps it does but which ones and how to use them, are not entirely clear. While other editors may have specialized features for this, Gimp does not. Gradient removal and star reduction are some of the main issues when processing astrophotography images.
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