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Lens-Free Flat Cameras Make Use of Pinhole Technology (npr.org)
RhubarbPye writes: As reported on NPR, "Engineers in Texas are building a camera that can make a sharp image with no lens at all." By incorporating millions of individual pinholes with photoreceptors and postprocessing software, this camera system has been reduced to minimal thickness. Cameras in the wallpaper? A new phase of wearable cameras? What other applications for this technology could be developed?
So, let's see if I get this right. They rediscovered something, that everyone from the 1990's and 80 years prior learned to make as part of science class...and simply applied modern technology to it.
No, you didn't get it right. But that's not surprising, clearly you didn't read the article.
If you want news from today, you have to come back tomorrow.
If you have to solve a big giant matrix inversion to do the job of a collimating lens, you're composing each pixel as a sum of many others instead of just itself, some of them being way brighter than the reconstructed image, meaning your reconstructed pixel is always noisier.
Not really.
When you average a large number of samples the noise tends to partially cancel out while the signal keeps adding up. Though the noise goes up with more samples, the signal goes up more, improving the signal to noise ratio. Even if you end up adding in some bright signals, with extra noise, that's still stomped by signal when you have enough samples.
Bantam Dominique roosters crow a four-note song. Once you've heard it as "Happy BIRTHday" you can't NOT hear it that way
No, the original poster was more correct. They're not averaging together a bunch of pixels, but applying an inverse matrix , which will weigh pixels differently, and quite frequently can involve very high weights assigned to noisier signals. This can result in an emphasis that amplifies noise. There is a lot of work done on different ways of clipping or modifying such matrix equations to make it slightly less accurate in an ideal world, but much less noisy in the real world.
Also, no averaging of noise will happen if you try to produce images with similar pixel count to the number of detectors. And if you do use more sensors than resulting pixels, the averaging increases signal to noise ratio only in general when the noise is independent, but in this case there will be some strong correlations that can lower that signal to noise ratio in some cases.