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Additionally, we calculate the normalized squared error (NSE) between the estimated RTFs and the ideal RTF calculated from the measured RIR to evaluate the estimation of the RTF.
We used SNR, PSNR, RMSE, and NCC to evaluate the estimation quality of separated images.
In this subsection, raw data of a single target are simulated to evaluate the estimation accuracy.
One metric to evaluate the estimation accuracy is the normalized root-mean-square error (NRMSE) [21].
The first criterion is used to evaluate the estimation errors of the functions.
In order to evaluate the estimation errors, a nonlinear hypocenter determination procedure (Tarantola and Valette 1982, Miyamachi and Moriya 1987) was applied.
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We checked the performance of our analysis using data from well-relocated events that occurred in the area of Step 4 and are used for analyzing travel-time residuals, and evaluated the estimation error by comparing the estimated hypocenters with those listed in the JMA hypocenter catalogue.
The simulation takes a pair of filters one at a time, computes the camera responses using Equation 1, obtains the estimated spectral reflectance using four different spectral estimation methods and evaluates the estimation errors (spectral and colorimetric) as discussed previously.
The root mean square error is adopted to evaluated the estimation performance.
Then, we statistically evaluated the estimation process quality when using a least squares approach.
We aim at evaluating the estimation potential of recently presented filters grounded in the Maximum Correntropy Criterion (MCC).
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