Please see
bulrichl's calibration guide for the
correct way to perform data reduction in PixInsight, along with important and rigorous information on many essential topics. Instead of doing the image calibration and registration tasks manually, you can use the WeightedBatchPreprocessing script, which automates them. However, knowing the entire data reduction process and understanding how it works at each stage is absolutely necessary if you want to control your data. Automation should never come without previous knowledge of the tasks performed.
LocalNormalization is an advanced tool able to provide good improvements in difficult cases. However, this is only true when it is used correctly, and only when it is really necessary:
- Please do not use it on a regular basis, unless you have good reasons to use it. Some of them are gradients with varying orientations and relatively small differences caused by varying atmospheric conditions. If in doubt, don't use it. LocalNormalization is an advanced tool, it is not a necessary task.
- Never use it on dissimilar images. For example, don't use LN on narrowband and broadband images, or on very different images for any other reason, including significant exposure times. This does not make any sense.
- Never use it blindly with default parameters. The tool is complex and usually requires fine tuning of parameters and some trial-error work.
- Always inspect the resulting images after LN. The LN task is ill-posed by nature, so never take for granted that the implemented algorithms will succeed. The LocalNormalization tool provides many resources for quantitative and qualitative evaluation of results, which should always be used.
If you are starting, keep things simple and try to learn correct, robust procedures. You probably don't need LocalNormalization for this data set, and shouldn't use it at this point.
