Timeline for Normalisation of an 'image' when pixel intensities are unbounded
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 25, 2018 at 14:01 | answer | added | kbrose | timeline score: 1 | |
| Jun 24, 2018 at 23:37 | comment | added | Sycorax♦ | Conventional re-scaling to a bounded interval uses only max and the min. There's no reason you're restricted to these options. You could re-scale by transforming the ecdf. The ecdf for an absolutely continuous distribution is, by construction, uniform between 0 and 1. | |
| Jun 24, 2018 at 22:11 | comment | added | Sycorax♦ | Note that dividing everything by the largest intensity would only enforce an upper bound at 1, but do nothing to enforce a particular lower bound. It seems that your stuck between trying to fit a square peg in to a round hole (rescaling everything to $[-1,1]$ so that you can use a GAN) or else you would need to use a model that is more flexible in the kind of data it can accommodate. But maybe re-scaling in this way isn't necessary -- someone could have written a paper on the topic. Or maybe you could try fitting the model and decide whether or not it's good enough. | |
| Jun 24, 2018 at 22:08 | comment | added | Ananda | To be clear, you mean divide everything by the largest intensity value in the entire image. Right? I did think of that but if you do so, then you model will never generate any intensity larger than that right? Also, the largest quantity is several order or magnitudes larger than the average value. Is it really wise to divide everything by such a large number? | |
| Jun 24, 2018 at 22:03 | comment | added | Sycorax♦ | Just use the largest and smallest values observed in the data. | |
| Jun 24, 2018 at 21:58 | history | asked | Ananda | CC BY-SA 4.0 |