9 ms·
Yes. It's the same reason you can't take 10 out of focus pictures of yourself and get one in focus picture. You need actual good data to combine.
by geocrasher 5y ago
Yes. It's the same reason you can't take 10 out of focus pictures of yourself and get one in focus picture. You need actual good data to combine.
- jfengel 5y agoYou can, with enough processing (and knowledge of how out of focus it is). It worked for Hubble. But you get more good pictures if you get it in focus in the first place.
- Keyframe 5y agoDeconvolution. Haven't followed that in awhile, but getting to original function was next to impossible. Recording calibration image and/or recent AI developments might've taken that into something awesome. I'll go and catch up on material!
- toss1 5y agoWe really need to watch it with trusting AI. While my example here isn't photo recognition, the same principle applies. I recently sat for a deposition where the stenographer used an "AI"transcription system. The result was literally pages of errata (vs the standard errata sheet that has space for about a dozen lines). The consistent error I noticed was that the erroneous words were (probably) the word most expected in that position, and NOT the word that I said. So, at a glance, it seemed like a really good transcription. In fact, many errors were barely noticeable to me and I had to go back to the audio recording to confirm. And these were errors that substantially changed the meaning, or even inverted it. This is not merely information loss — the least surprising/lowest information item was inserted instead of the real item — this is actual information CORRUPTION. I'd fully expect parallel phenomena from image - "AI - filling in the item most expected from the training set, and actively corrupting the data by stripping out the highest-value info bits and replacing them with the most expected. Beware
- Keyframe 5y agoYeah, it's called signal reconstruction for a reason. There are classes of it where it's verifiable and decent enough however.
- toss1 5y agoYes, I'm sure that with properly constrained and well-tested data spaces, it could produce outstanding and very helpful results. But accurate reconstruction in the wild is just sooo far away. And for good reason - it would need to have insane amounts of experience and exposure to every bit of unusual data that existed in the world to get it right...
- toss1 5y agoYup A engineering friend of mine was working on hardware related to mil satellite imagery, and was sent to a course that covered all the kinds of post-processing techniques they had to improve resolution and what could help those techniques upstream. He said that at the end of the course, the instructor said the bottom line was that while they could do all kinds of 'magic' to improve & enhance the photos, the best input to all their techniques that would yield the best end result, was to take a better photo in the first place. So, yes, there really is no substitute to a good original image.
- dekhn 5y agoas long as you know the point spread function you can focus out of focus images through deconvolution. https://imagej.net/imaging/deconvolution https://imagej.net/imaging/deconvolution Basically, take a picture of a well-known object that should look like a single pixel, measure how spread out it is, and then use that to backconvert images taken from the same camera.
- geocrasher 5y agoWould you prefer this approach over actually having the target in focus?
- dekhn 5y agoIt depends. Personally, I'd apply the technique even to focused images, after using a set of fluorescently labelled beads of known diameter to calibrate the PSF.