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
(MSEfree) and noise dependent MSE (MSEnoise) were not discussed separately before. The MSEfree depends on the
spectral characteristics of a set of sensors, illuminations and reflectances of imaged objects and the MSEfree arises in the
noise free case, however MSEnoise 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 MSEnoise 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 MSEnoise and the experimental results to show the trustworthiness of the
expression for the regression model, Imai-Berns model and finite dimensional linear model.
The noise present in a color image acquisition system influences the accuracy of the estimated colorimetric values and the accuracy of the recovered spectral reflectances of objects being imaged through the use of sensor responses. Estimation of the noise levels in the devices is important for the accurate acquisition of colorimetric or spectral information. This work addresses the problem for the determination of noise variances in multispectral image acquisition systems. Several models for the determination are compared and experimental results to show the accuracy of the model proposed by the author are demonstrated. It is shown that the estimates by the proposal agree fairy well with the noise variance which minimizes the mean square errors (MSE) of the recovered spectral reflectances by the use of the Wiener filter.
The acquisition of the colorimetric information about an object using a color image acquisition device is important at an early stage in a color management system. The accuracy of the colorimetric values estimated by the device responses depends not only on the spectral sensitivities of a set of sensors but also on the noise present in the devices. We address the optimization of a set of spectral sensitivities with Gaussian distribution functions based on a colorimetric evaluation model. It is demonstrated that the design of optimal sensors is contingent on finding the right balance between the human visual subspace and the subspace that maximizes the singular values of a matrix 1/2 to increase the robustness to noise, where , , , and represent a sensor matrix, a diagonal matrix for an illuminant, a basis matrix, and a diagonal matrix with eigenvalues of an autocorrelation matrix of reflectance spectra, respectively.
The reconstruction of spectral reflectances of objects being imaged is important in reproducing a color under a variety of viewing illuminants. In this work, a simple formula is derived to evaluate the quality of a set of color image sensors aimed at reconstruction of spectral reflectances, and it is applied to multispectral image acquisition systems. Since the quality depends not only on the spectral sensitivities but also on the noise present in the systems, it is impossible to evaluate a set of sensors without prior knowledge of the noise present in it. Therefore, the noise variance of the multispectral cameras is estimated by a new proposal, and it is applied to the evaluation for the first time. It is shown that the experimental results agree well with the predictions by the evaluation model, and that the method to estimate the noise variance is useful for the evaluation.
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