TL;DR. Most uncooled camera chips give you maybe 10 or 11 bits of dynamic range, and light is subject to Poisson noise, meaning the brighter a pixel, the noiser it is in absolute (not relative) terms. 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. Cool idea, and certainly has its a
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.
No. As someone with patents on multiplex imaging, I can tell you that inverse problems lead to greater noise or equivalently reduced dynamic range. You can see this in their photos.
Dynamic range? (Score:5, Insightful)
Re: (Score:5, Informative)
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.
Re:Dynamic range? (Score:2)
No. As someone with patents on multiplex imaging, I can tell you that inverse problems lead to greater noise or equivalently reduced dynamic range. You can see this in their photos.