In addition to the Signal to Noise Ratio (SNR) the noise can also be characterized through its frequency spectrum, which shows the distribution of the noise in the frequency domain, usually through the use of Fourier Transform. The representation of the noise in the frequency spectrum (Noise Power Spectrum, NPS), also called the density spectrum (Power Spectrum Density, PSD) shows the distribution of the noise, in terms of density, in relation to its frequencies. That is, the frequency of the noise in the image will take more or less presence depending on its spatial frequency. As in the SFR-MTF the frequency is the strategy to quantify the capacity to reproduce the detail, those artifacts that exceed certain frequencies will be barely perceptible by our vision system. Therefore, the representation of the noise through its frequency spectrum has an important correlation with the appearance of the noise.

In reality we not only represent noise but we represent any kind of spatial feature present in the image. It can be useful to estimate the frequency of appearance of elements present in the image as well as issues related to the perception of detail, etc. Thus an image can be represented through the domain of space, that is in its conventional x and y coordinates, or in the domain of frequency. In both domains, we can discern different aspects of the noise. Noise of random nature, which attends to models such as Gauss, can be studied by means of the representations in the histogram, which organize the information of the image by frequency of appearance, so this panel also offers a representation of the histogram of the selected ROI