Noriyuki Shimano

Recovery of spectral reflectances of imaged objects by the use of features of spectral reflectances

Recovery of spectral reflectances of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. Although the Wiener estimation is usually used for the recovery, the recovery performance of the estimation depends on the autocorrelation matrix of the spectral reflectances and the noise present in an image acquisition system. The purpose of the present paper is to show that the Wiener estimation with the noise variance estimated by the previous proposal [IEEE Trans. Image Process. 16, 1848 (2006)] and with the autocorrelation matrix that uses the features of the spectral reflectances recovered by the previous method is very effective in greatly improving the performance.

Estimating the noise variance in an image acquisition system and its influence on the accuracy of recovered spectral reflectances

It is well known that the noise present in an image acquisition system plays important roles in solving inverse problems, such as the reconstruction of spectral reflectances of imaged objects from the sensor responses. Usually, a recovered spectral reflectance vector r^ by a matrix W is expressed by r^=Wp, where p is a sensor response vector. In this paper, the mean square errors (MSEs) between the recovered spectral reflectances with various reconstruction matrices W and actual spectral reflectances are divided into the noise independent MSE (MSEFREE) and the noise dependent MSE (MSENOISE). By dividing the MSE into two terms, the MSENOISE is defined as the estimated noise variance multiplied by the sum of the squared singular values of the matrix W. It is shown that the relation between the increase in the MSE and the MSENOISE agrees quite well with the experimental results by the multispectral camera, and that the estimated noise variances are of the same order of magnitude for various matrices W, but the increase in the MSE by the noise mainly results from the increase in the sum of the squared singular values for the unregularized reconstruction matrix W.

Noise robustness of a colorimetric evaluation model for image acquisition devices with different characterization models

Colorimetric evaluation of an image acquisition device is important for evaluating and optimizing a set of sensors. We have already proposed a colorimetric evaluation model [J. Imaging Sci. Technol. 49, 588-593 (2005)] based on the Wiener estimation. The mean square errors (MSE) between the estimated and the actual fundamental vectors by the Wiener filter and the proposed colorimetric quality (Qc) agree quite well with the proposed model and we have shown that the estimation of the system noise variance of the image acquisition system is essential for the evaluation model. In this paper, it is confirmed that the proposed model can be applied to two different reflectance recovery models, and these models provide us an easy method for estimating the proposed colorimetric quality (Qc). The influence of the system noise originates from the sampling intervals of the spectral characteristics of the sensors, the illuminations and the reflectance and the quantization error on the evaluation model are studied and it is confirmed from the experimental results that the proposed model holds even in a noisy condition.

Estimating the noise influence on recovering reflectances

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.