Calculation of Residual Points, Mean Square Error, Signal-to-Noise Ratio, and Edge Equivalent Number of Looks

This document provides implementations for four key metrics: residual point calculation, mean square error computation, signal-to-noise ratio measurement, and edge equivalent number of looks estimation. For residual point calculation, we process each data point's residual value using threshold-based detection and aggregate the total count of significant residual points, typically implemented through iterative scanning and conditional counting algorithms. The mean square error calculation evaluates data dispersion by computing the average of squared differences between data points and their mean value, often implemented using vectorized operations for efficient computation. Signal-to-noise ratio calculation assesses signal reliability by comparing signal power to noise power, commonly implemented through Fourier analysis or wavelet decomposition for frequency-domain separation. Edge equivalent number of looks estimation determines edge clarity and contrast for image quality assessment, typically involving gradient magnitude calculations and statistical analysis of edge regions using convolution operators like Sobel or Prewitt filters.