From what I have understand from your question, I think you are looking for a way to transform a probability distribution to another probability distribution. For example, if your first hash function distributes your data into a gaussian/normal form into the buckets, then you need a function that takes the index of this bucket as input and gives you another bucket which must be chosen uniformally. This post may be helpful if I ve understand correctly what you are trying to achieve: https://stats.stackexchange.com/questions/223317/how-to-transform-one-pdf-into-another-graphically
My personal opinion is that a successful transformation depends a lot in your data. A hash function tries to distribute the data uniformally in the first place. But, even if you have a function G that distributes your data in a "weird" way then you can make a transformation T: G -> F so that F is whatever distribution you like.
I don't think that this is achievable without using randomization. For example, if you have a hash function that maps all your data into one bin (for example the bin 0) then this function is constant. So, you are trying to create a function that takes a constant value and maps it uniformally, which cannot be done if you don't use any randomness or internal memory. By internal memory, I mean that your function "should remember" what kind of inputs has already "seen", and map them in a different value each time.