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sharkmelley

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About sharkmelley

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  1. I would not expect that to happen. After converting the 32-bit image to a 16-bit image check the noise level (i.e. the standard deviation) in a small area of background of the 16-bit image. You should find it adequately dithers the quantisation. If so, then it means that the reduction to 16-bit is not the cause of your posterization issue. No amount of stretching will introduce posterization in an image where the quantisation is adequately dithered by noise. Mark
  2. That's a clever solution and a good write-up! Mark
  3. Your "per pixel" metric is misleading you. A better metric is the total number of photons captured by the whole sensor. My advice is to choose whether you want to go OSC or Mono and then choose a large sensor. Otherwise you will miss the field-of-view of your Canon. Mark
  4. I just accidentally found a document that defines gamut of a sensor. See section 2.1: https://corp.dxomark.com/wp-content/uploads/2017/11/EI-2008-Color-Sensitivity-6817-28.pdf So contrary to what I thought, the concept does exist! Mark
  5. Can you clarify what you mean here. I agree that with set-point cooling, darks don't need to be scaled (if you match exposure times). But that doesn't mean dark scaling can't be done for CMOS. Mark
  6. I know exactly what you are trying to do here. Funnily enough, earlier this week I had a disagreement with someone on another astro-forum who called the Bayer filters "sloppy" because their transmission bands overlap! It's obvious to most people (but not to the contributor to that forum) that the sharp cut-off RGB filters typically used for astro-imaging are inferior for colour reproduction. The example of trying to image a rainbow is a great example of this. The sharp cut-off RGB filters cannot reproduce the continuous change of colour within the rainbow. But this is not a problem of gamut. Gamut applies to display devices. For instance an LED display can reproduce all the colours within the colour triangle formed by its Red, Green and Blue LEDs. This is its gamut. However, a camera with RGB filters can be considered to be full gamut because it is able to record all those colours i.e. there is no colour it is unable to record unless there are gaps between the filter transmission bands. The problem it has is the inability to distinguish between a wide range of colours i.e. many different colours give exactly the same RGB pixel output values from the sensor. The concept you need is "metameric failure". This is the inability of the camera to distinguish between colours that the human eye sees as being different. Those who test consumer cameras will report a "sensitivity metamerism index" (SMI) for the camera which is a standard way to measure its colour accuracy. Mark
  7. It's a good way of telling if someone has added diffraction spikes to their colour image during post-processing. Such diffraction spikes are typically uniform in colour and won't have the colour banding effect. Mark
  8. If the fractionally binned image is split into 4 sub-images then you're right in saying that there is no correlation between them. I'm seeing the same standard deviation (on average) in each of the 4 sub-images as I do in the fractionally binned image. Mark
  9. I'm very late to this thread but it looked interesting. Did you find your problem? In any case I simulated this in a spreadsheet and found that 1.5x binning leads to a SNR reduction of 1.8x, which is what you expected. I generated 10,000 groups of 9 random values. Each group of 9 values was binned down to 4 values using your algorithm. This created 10,000 groups of 4 values. The SNR of the resulting 40,000 values was 1.8x lower than the SNR of the original 90,000 values. Mark
  10. That's a very informative video and an interesting solution to the problem of using filters with the Nikon Z cameras. The approach probably works well for lenses but I think 1.25" filters will cause vignetting issues when attaching the camera to a scope. Mark
  11. Drizzle works best on undersampled images. Standard (random offset) dithering is all that is required. I use Bayer Drizzle (sometimes called CFA Drizzle) as part of my standard processing workflow and it's especially beneficial for OSC cameras or DSLRs because it increases resolution by avoiding the interpolation that takes place during debayering. The other advantage it gives is a more finely grained noise structure than standard stacking, which helps with noise reduction. It's definitely well worth a try! Mark
  12. For diagnostic purposes, I've boosted the saturation in your image: Notice that the rings are much more like polygons than circles. Those polygons are a sure sign this is caused by the lens correction issue on Sony cameras. Mark
  13. As others have said, the dark patches are caused by dust. The concentric coloured rings are most likely caused by Sony's crude lens vignetting ("shading") correction. There are lots of threads about this e.g. https://www.dpreview.com/forums/post/62199570 It helps to switch off lens corrections but even so, you can't totally get rid of it because there are remaining "corrections" that cannot be switched off. Mark
  14. I've never seen the Cocoon so deep! Fabulous image! Helped by the fast optics of the Tak Epsilon of course Mark
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