FluxSmooth

slk001 · 19th November 2002, 16:52

Assuming they aren't noise too .

True, so true... in fact, this is one assumption that you have to make when you are going to try to replace the noise with what you think is "good" data.

This is one approach, but it has the disadvantage that it tends not only to reduce more noise but also destroy more details.

Well, not really. If your pixel under test value is "8", and its temporal neighbors are both "5", then the probability that this pixel is noise and not detail data is quite high (remember, my vision of this filter would be to NOT FILTER if the temporal neighbors exceeded a threshold, and detail is NOT LIKELY to be only one frame long). But this technique would also almost completely remove single frame movements (like the slashing sword). That is why I believe an additional test of the pixels SPATIAL neighbors ABOVE and BELOW (although maybe not DIRECTLY) would be needed.

Unfortunately since noise is inherently random, we can't be sure of anything like that.

Again, this is so true. Unfortunately, we have to put a limit on how much we expect our filter to do. If the source is getting too noisey, then we have to direct our filter to shut down.

Just because the pixel was originally a 5 and is now an 8 doesn't make that pixel's value useless -- we just need to correct it, and that's the hard part.

When noise corrupts a signal, it tends to not just slightly modify the value, but instead tends to send the value to one of the luminance rails (16 to 235). This is where, of course, averaging is less than perfect.

You mean distributed over several horizontal pixels?

For TV, the answer is yes. This is because TV is sent out serially and noise, when it occurs, affects a "serial string" of data. This means that determining an average with a pixel's 8 spatial neighbors is likely to contain corrupted data in 3 of them. However, if this spatial averaging is done with the top 3 and bottom 3 neighbors, only 1 is likely to be corrupted.

The type of noise that I am interested in filtering, is the random (but sometimes heavy) IMPULSE noise, like from ignition noise or lightning strikes. These are perfect candidates for my replacement "theory" of filtering as opposed to simply trying to average it out.

The filter I envision would probably fail miserably against a picture with heavy picture snow (as would most noise filters). Here, the only alternative would be the averaging techique that you propose.

I can send you a clip of the type of noise that I am talking about, if you are interested.

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19th November 2002, 16:52	#11 \| Link
slk001 Registered User Join Date: Apr 2002 Posts: 399	Assuming they aren't noise too . True, so true... in fact, this is one assumption that you have to make when you are going to try to replace the noise with what you think is "good" data. This is one approach, but it has the disadvantage that it tends not only to reduce more noise but also destroy more details. Well, not really. If your pixel under test value is "8", and its temporal neighbors are both "5", then the probability that this pixel is noise and not detail data is quite high (remember, my vision of this filter would be to NOT FILTER if the temporal neighbors exceeded a threshold, and detail is NOT LIKELY to be only one frame long). But this technique would also almost completely remove single frame movements (like the slashing sword). That is why I believe an additional test of the pixels SPATIAL neighbors ABOVE and BELOW (although maybe not DIRECTLY) would be needed. Unfortunately since noise is inherently random, we can't be sure of anything like that. Again, this is so true. Unfortunately, we have to put a limit on how much we expect our filter to do. If the source is getting too noisey, then we have to direct our filter to shut down. Just because the pixel was originally a 5 and is now an 8 doesn't make that pixel's value useless -- we just need to correct it, and that's the hard part. When noise corrupts a signal, it tends to not just slightly modify the value, but instead tends to send the value to one of the luminance rails (16 to 235). This is where, of course, averaging is less than perfect. You mean distributed over several horizontal pixels? For TV, the answer is yes. This is because TV is sent out serially and noise, when it occurs, affects a "serial string" of data. This means that determining an average with a pixel's 8 spatial neighbors is likely to contain corrupted data in 3 of them. However, if this spatial averaging is done with the top 3 and bottom 3 neighbors, only 1 is likely to be corrupted. The type of noise that I am interested in filtering, is the random (but sometimes heavy) IMPULSE noise, like from ignition noise or lightning strikes. These are perfect candidates for my replacement "theory" of filtering as opposed to simply trying to average it out. The filter I envision would probably fail miserably against a picture with heavy picture snow (as would most noise filters). Here, the only alternative would be the averaging techique that you propose. I can send you a clip of the type of noise that I am talking about, if you are interested.