You've got traditional iterative methods through to bayesian methods and machine learning methods. Each of these approaches seemed to be relatively isolated from each other and largely cited from their own subfield, despite all solving variants of the same problem. When they did cite outside the field, they tended to cite less relevant or far from state-of-the-art papers.
What was amazing was the number of papers where you would have a plot showing how amazing the new deconvolution method was in the presence of noise compared to other methods, often using a vaguely defined metric. This plot would take up about half a page, usually following a few example images that were set very very small, often ~2cm and usually in a grid. That was always deeply suspicious.
The other thing I remember were models with tons of unexplained parameters, which were carefully tuned to give good results for that image. But really it was guesswork. Sometimes the authors would carefully avoid well-cited work in another field that they could easily have found.
As a tinkerer with a scarcity of time you learn where not to waste your time and move on.