Abstract
The evaluation of the noise present in the image acquisition system and the influence of the noise is essential to image acquisition. However the mean square errors (MSE) is not divided into two terms, i.e., the noise independent MSE (MSE<sub>free</sub>) and noise dependent MSE (MSE<sub>noise</sub>) were not discussed separately before. The MSE<sub>free</sub> depends on the spectral characteristics of a set of sensors, illuminations and reflectances of imaged objects and the MSE<sub>free</sub> arises in the noise free case, however MSE<sub>noise</sub> originates from the noise present image acquisition system. One of the authors (N.S.) already proposed a model to separate the MSE into the two factors and also proposed a model to estimate noise variance present in image acquisition systems. By the use of this model, we succeeded in the expression of the MSE<sub>noise</sub> as a function of the noise variance and showed that the experimental results agreed fairly well with the expression when the Wiener estimation was used for the recovery. The present paper shows the extended expression for the influence of the system noise on the MSE<sub>noise</sub> and the experimental results to show the trustworthiness of the expression for the regression model, Imai-Berns model and finite dimensional linear model.
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