Help with automated baseline removal
Baseline removal for signals and background filtering in images  remain, as far as I am aware of, an ill-posed  problem related to source separation. Reasons are legion, mostly around the unresolved question: "how to measure the range of admissible baselines both qualitatively (for the signal specialist) and quantitatively (for the objective)?". 

If from one measurement at a time, you want to separate two or three components, this is an under-determined problem. Using asymmetry already induces at least one asymmetry parameter. Using any prior or constraint (gaussianity, positivity, peak symmetry, sparsity) is likely to yield one more parameter each, generally linking two quantities of different dimensions. And even if my objective target is clear, which is seldom the case, many solutions could be valid. For instance, if I am interested in  peak area, the baseline below is fine, yet shocking for the data analyst:

 

At the present time I see two main options:


try to fit baselines globally, not individually, using optimization and algebraic tools (low-rank, NMFetc.) but as numerical data has limited precision and quantization (integer, float), some constraints have to be (machine-dependent) fixed,
try to learn baselines of "meaningful subsets", but you have to choose their size, how to weight them, etc. Even deep learning needs a little tuning,
try to guess parameters from signal-based heuristics, and a little grid searching.


I admit I still stick to the third option, not yet successful in most instances. Check  How to remove or filter the drift problem in measured Strain signal? for a related question and  answers.