If someone shows me a photograph of a tree and it contains variations in tonality or color that don’t jibe with my expectations, I will interpret these variations-especially if they are of relatively high spatial frequency-as noise. Thus, I have detailed and inflexible ideas regarding what trees ought to look like. I’ve looked at countless different trees on countless different occasions under a wide variety of lighting and atmospheric conditions. Image data, on the other hand, are often in direct competition with the “official” representation produced by the human vision system. If I’m using a thermistor to collect temperature data, I don’t have a strong expectation for what the resulting voltage signal should “look like.” I can display the sensor signal on a scope, and I might notice some high-frequency variations that are likely to be noise, but the appearance of the waveform doesn’t really offend, so to speak, my preconceived ideas about the characteristics of this particular thermistor signal. However, there’s an interesting consideration that comes into play when we’re dealing with imagery. That same definition applies to noise in visual information as well. In my article on electrical noise, I defined noise as undesirable voltage or current variations that are (often) random and (usually) of relatively low amplitude. Pixel readout and frame rate in CCD imaging systems.Sampling, amplifying, and digitizing CCD output signals.CCD types (e.g., full-frame, interline-transfer, and frame-transfer).Welcome to Part 11 of the AAC series on CCD (charge-coupled device) image sensors! Before moving on to this article on dark noise, please check out the links below to catch up on any of the topics we've covered so far:
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