Making images taken under daylight conditions appear as if they were taken at night using image processing elementary techniques I know that these days' style-transfer GANs should be able to style-transfer day images into night images. I however prefer to do it using a set of elementary image processing manipulations, so I can control their random parameter ranges.

I have a small dataset of images taken at night, which are very hard to train on with transfer-learning from an ImageNet pre-trained network. This is kind of making sense, as ImageNet images are usually taken in daylight conditions.

I therefore want to try to create a "night version" of ImageNet, by applying elementary image-processing manipulations on it (e.g., adding noise, shrinking the color histogram and reducing its mean, etc.). That way, I hope I will be able to train a network that will be useful for transfer-learning on night images.

Are there known algorithms/methods for doing that daylight->night conversion using image processing?
That really doesn't make sense: you'd be training a neural network to mimic a conversion algorith, which you already have. That's a waste of electricity ;) I doubt the resulting nets would generalize at all: you're not even training them with actual day/night picture pairs. So, really, as usual: one of the most significant problems in machine learning is getting a good set of input data. 

In your case, that's still pretty easy (just take a couple hundred pictures from the same positions at day and night – you can basically do that in a week, if you have a camera that you can reliably bring to the same positions). So, don't skip on that crucial step.