[X] My Assistant

[JRehling]

I recently saw a PBS-produced show which was quite off-topic for this board, generally, but involved image processing. This special, "The Bomb", showed a lot of old film footage of nuclear explosions. Where this is relevant to the board is that they cited new work in image processing that dramatically improved the visual quality of that film. Early on, they showed old, grainy film, and showed the quality of the improvements as a "scan line" moved across the frame, converting the old, grainy imagery to new, greatly improved video. They verbally referred to this image processing work, but I'm not sure how one would find out the details of the work.

The show itself is cited here:

http://www.pbs.org/program/bomb/

[JohnVV]

i have been using it for many years .Gmic has mostly replaced imagemagick for me ( except for matlab m files )

the gimp plugin is 8 bit ( gimp 2.8 ) with support for 16 and 32 bit and float images in Gimp 2.9 DEVELOPMENT

but the terminal version runs on { uchar | char | ushort | short | uint | int | ulong | long | float | double } image data
so from ascii text and cvs data to 8 ,16, 32,64 bit data

an example from Voy2 at Neptune
Gimp 2.9.3 DEVEL , gmic ,resynthesizer, (and the built in HEAL tool )

[JRehling]

As I'm starting to get into astrophotography, I'm finding that one useful tactic comes from an understanding of whether noise is systematically brighter or darker than the signal.

If noise may cause pixels to end up either darker or lighter than the signal, then algorithms that average values (over space or time) may be the best way to remove glitches.

But if you know that noise is brighter than the signal (for example), averaging may not be as good as combining values (over space or time) with a "minimum" operator. Or, obviously, a "maximum" operator if the noise is systematically dark.

When I take images of, say, the Messier objects, there is a fuzzy boundary between the (theoretically) black sky and the gray object. My camera introduces noise which is almost always brighter than the signal. A naive approach could be to bin pixels 2x2, so the noise is spread out among 4 times as much signal, at the cost of resolution. But I've found it is much better to wiggle the image in 4 copies – perhaps wiggling it by 2 pixels each time. Then, overlay those images and take the minimum value for each resulting pixel. Again, I lose resolution, but now the noise is not just reduced to 25% of what it was, but – in many cases – to zero!

I very much doubt if this is an original insight, but I came by it independently.

[HSchirmer]

But I've found it is much better to wiggle the image in 4 copies – perhaps wiggling it by 2 pixels each time.
Then, overlay those images and take the minimum value for each resulting pixel.

Eh, with the right computations, you can overlay multiple images
to synthesize a significantly higher resolution than each individual pixel.

Basically, taking 16 blurry photos of the same static image allows you to compute an image 16x the resolution.


https://www.researchgate.net/publication/24...the_L'_band

http://www.nature.com/nphoton/focus/superr....html#editorial