Weighted standard deviation function in r

WEIGHTED STANDARD DEVIATION FUNCTION IN R HOW TO

We can think about that RGB image, like composed by three monochromatic images with the same dimensions as the color image. We have a color image represented in the RGB color model, this means we have an image composed by color pixels, where each pixel has three numeric attributes representing the intensity (or luminosity) of the red, green and blue primaries, whose mix renders the corresponding pixel color. What follows is a better context description, then we will explore some solutions.

WEIGHTED STANDARD DEVIATION FUNCTION IN R HOW TO

The question is how can we get a noise measurements representative of the whole image? This means to solve questions as: how can we get the average and the standard deviation and the resulting SNR representative of the entire image? In other words, we want to know how to merge the three channels averages, standard deviations and SNRs to get only one set of those values representative of the entire image. For each of the three image RGB channels we can easily compute the average and noise to get the SNR, but this way we end up with three averages, standard deviation, and SNR values: One set of those values for each channel. We know that a key indicator of the noise in a image is its SNR (signal to noise ratio), which is computed as the average color value of the pixels in a plain patch target divided by the standard deviation (noise) of the color values of those pixels. We want to measure the noise in a RGB image. Photoshop Average and Standard Deviation Formulas.

Weighted standard deviation function in r