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#21 | |||
Senior Member
Join Date: Mar 2003
Posts: 205
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Here is a crude example of a bayer sensor: R=red G=green B=blue RGRG GBGB RGRG GBGB Each of the individual R, G, and B sites is used by the demosiacing algorythym to estimate/solve the approximate real color that each pixel should compose. The result is a R, G and B at each of those points. X=Red, Green and Blue XXXX XXXX XXXX XXXX Of course, this leads to color errors and reduced resolution, as compared to if you had a R, G and B sensor at every point originally. This is why a 3.5MP Foveon sensor resolves greater detail then a bayer sensor. It has R, G and B sensor at every spatial sampling point. No demosiacing is required. Quote:
Here is an example of an aliasing byproduct: ![]() Look at 14 mark, their are 9 distinct lines. Look at the 20 mark. Their are now 5 or so lines and they are thicker then the top lines. IN reality, the chart had 9 distinct lines to the 20 mark that gradually got thinner. When the spatial frequency of the chart exceeded the nyquest limit of the sensor(which has not anti alias filter), the sensor misread the multiple high frequency lines and falsely displays a lower frequency modulation. Result is a false pattern. This will happen on any pattern that exceeds the spatial frequency of this sensor. This is only an example that lets you clearly see one effect of aliasing. The other effect is jaggies(staircaising) on some high contrast edge transitions on curves and diagnols. -Chris [/img] |
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#22 |
Senior Member
Join Date: Jan 2004
Posts: 149
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This is wonderful information, but should all of this be in the "Newbie Help" section?
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#23 |
Junior Member
Join Date: Mar 2004
Posts: 10
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Perhaps not as it is very detailed however it is pixel related, I'm a learning newbie and am very grateful for everyone's input in helping me to understand. Thanks.
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