Siril 1.0

Normalization will adjust the levels of each image against the reference image. This is particularly useful for mean stacking with rejection, because rejecting pixels if the images show differences of levels is not very useful. They can be caused by light nebulosity, light gradient caused by the moon or city lights, sensor temperature variation and so on.

There are four types of normalization available. With Additive, images are aligned by adding a constant to all pixels of an image. This offsets the background level. Multiplicative will multiply pixel values, like the linear match algorithm, to make stars the same brightness. Additive with scaling. Multiplicative with scaling.