4 ms·
A few years ago I found myself reading pretty much every paper on methods that could give a better looking solution to "deblurring" an image than the more estab
by dpwm 9y ago
A few years ago I found myself reading pretty much every paper on methods that could give a better looking solution to "deblurring" an image than the more established methods. This is really a field which has overlap with many different areas of research.
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.
- joe_the_user 9y agoHaving tried to find image-processing research/scholarship around 2009, before the neural net explosion really set-in, I have to say the field was a complete rag-bag of fragile, vague methods. And efforts to extend these didn't help them. Which is to say that no all incremental, scholarly research is necessarily what's needed. In fact, I think what the OP describes is effectively what Thomas Kuhn calls "normal science" [1], which can be great until it becomes terrible - ie, "normal science" periodically reaches crises that require a different approach (inspiration, insight, iconoclasm, etc, whatever). But that shouldn't detract from the point that anyone should know what traditional research, that if someone is going to engage in something other than normal science, they would benefit from being really aware of why and how they are doing this. [1] https://en.wikipedia.org/wiki/Normal_science https://en.wikipedia.org/wiki/Normal_science
- bigiain 9y ago"I have to say the field was a complete rag-bag of fragile, vague methods. And efforts to extend these didn't help them." You say that like it's not still state-of-the-art in machine learning and AI today. (And piloting self driving vehicles around public roads...)
- chestervonwinch 9y ago> a few example images that were set very very small, often ~2cm and usually in a grid Journals sometimes require the authors to pay "page charges" of sometimes hundreds of dollars per page. I know of some (relatively high impact) journals in image processing related fields that require this. If the author is not working under a grant, then there's not much incentive to add to the page length. Of course, this is insane that this is even a thing in the digital era, and it feels very mobbish. Idk. Maybe there's valid reason that I'm not aware.
- eastWestMath 9y agoIt’s funny how some disciplines banded together decades ago to found their own open access journals, but other disciplines are still train wrecks in that regard.
- sadid 9y agoAs a researcher, I'm agree with your point. One important aspect of science/scholarship is reproduciblity [1]. There is a crisis in current science regarding this. The response to this crisis is the open science movement. Some protocols on how this body of knowledge is maintained should be change but this takes time. [1]: https://en.wikipedia.org/wiki/Replication_crisis https://en.wikipedia.org/wiki/Replication_crisis