How the noises are measured.FPN is the component of pattern noise measured in the absence of illumination. ie means every dark image will have FPN am i correct.
MATLAB: How to identify noise in a captured image
dark noisefixed pattern noisefpn is the component of pattern noise measured in the absence of illumination ie means dark image will have fpn am i correct.Image Acquisition ToolboxImage Processing Toolbox
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You have to think about what type of noise you have - that is is it additive random intensity with no correlation on pixel-to-pixel scales; is it multiplicative; does the noise have a coherent structure, that is is there a recurring noise-pattern that extend across pixels, is your noise due to motion-blur, or an out-of-focus camera?
After answering the above question (hint: it is the coherent "noise") you have to think about how to best remove that type of noise. For random uncorrelated noise you would use some variant of spatial averaging (linear 2D filter, median-filter, sigma-filter (wiener2 in matlab), bilateral filters etc. etc...). For a coherent pattern-noise you would want to estimate the pattern and then remove as much of that as possible, for the case of harmonic interference-type noise that would be done most easily with a notch-filter in the Fourier-domain. For motion-blur or out-of-focus you would try to deconvolve away those effects, with the deconvwnr, deconvlucy etc....
To give you a final hint:
imagesc(log10(abs(fft2(sinimage))))There you should be able to identify your noise-components...
HTH
You index the arrays like we discussed in https://www.mathworks.com/matlabcentral/answers/360646-how-can-i-get-a-rgb-values-from-a-picture
Cameras are always affected by noise, so even if your object was at constant illumination, was larger than the field of view, was fixed distance, the lens parameters were not changed, and so on -- even in all of those conditions, not all of the pixel values will come out the same. The classic test for this is to put the lens cap on a camera and take a picture: you will seldom get out "all black" for all pixels.
There is essentially no such thing as "pure red" in physical objects. You can get hydrogen red as an emission from burning some things, or from lasers, but short of that you are dealing with absorption spectra based upon light with a distribution of photon energies. The light is being sensed by three photo receptors tuned to different but overlapping ranges. The photo receptors accumulate energy for a period of time. They do not recognize specific frequencies, they just accumulate energy -- which means that if you shone a different frequency that was only received half as efficiently but was twice as bright, then the receptors would not be able to tell the difference in energy. The conversion of relative energies over the period of time, into RGB values, is done under the model that a particular kind of light is being used; if a different kind of light was what was really used, then the conversion will be wrong.
It is the old effect of standing under a sodium vapor streetlight and looking terrible: your "real" color has not changed, but the illumination has. If your requirement held, that the colors were not to be affected by the environment, then it would follow that a picture of you would have to look the same regardless of whether you were standing in bright sunlight or if you were standing under one of those streetlights. You can (generally) make those kinds of color corrections if you use enough sensor types and measure the right things, but you really need special equipment.
Anyhow... Image Analyst has posted on how to do illumination correction.
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