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This study proposes a piecewise Wiener estimation method to reconstruct a spectral reflectance image from a three-band image by multipoint spectral information collected simultaneously with image acquisition. A three-band image is divided into several blocks and the spectral estimation is carried out using the Wiener estimation matrix assigned to each block. Each Wiener estimation matrix is constructed on the basis of spectral measurement data. The experimental results show that the proposed method reduces the average estimation error monotonically as the number of spectral measurements increases. In addition, the computational time of the piecewise Wiener estimation costs only several fold of the computational time of the conventional single-matrix method. OCIS codes: 100.2000, 100.3010, 110.4234, 330.1690.

Accurate color image reproduction under arbitrary illumination can be realized if the spectral reflectance functions in a scene are obtained. Although multispectral imaging is one of the promising methods to obtain the reflectance of a scene, it is expected to reduce the number of color channels without significant loss of accuracy. This paper presents what we believe to be a new method for estimating spectral reflectance functions from color image and multipoint spectral measurements based on maximum a posteriori (MAP) estimation. Multipoint spectral measurements are utilized as auxiliary information to improve the accuracy of spectral reflectance estimated from image data. Through simulations, it is confirmed that the proposed method improves the estimation accuracy, particularly when a scene includes subjects that belong to various categories. OCIS codes: 100.3010, 100.3190, 110.2990, 300.6550.

A high-performance positron emission tomography (PET) scanner, which measures depth-of-interaction (DOI) information, is under development at the National Institute of Radiological Sciences in Japan. Image reconstruction methods with accurate modeling of the system response functions have been successfully used to improve PET image quality. It is, however, difficult to apply these methods to the DOI-PET scanner because the dimension of DOI-PET data increases in proportion to the square of the number of DOI layers. In this paper, we propose a compressed imaging system model for DOI-PET image reconstruction, in order to reduce computational cost while keeping image quality. The basic idea of the proposed method is that the DOI-PET imaging system is highly redundant. First, DOI-PET data is transformed into compact data so that data bins with highly correlating sensitivity functions are combined. Then image reconstruction methods based on accurate system modeling, such as the maximum likelihood expectation maximization (ML-EM), are applied. The proposed method was applied to simulated data for the DOI-PET scanner operated in 2-D mode. Then the tradeoff between the background noise and the spatial resolution was investigated. Numerical simulation results show that the proposed method followed by ML-EM reduces computational cost effectively while keeping the advantages of the accurate system modeling and DOI information. Index Terms--Depth-of-interaction (DOI), imaging system model, maximum likelihood expectation maximization (ML-EM), positron emission tomography, statistical image reconstruction.

Nonlinear estimation method of spectral reflectance from camera responses is proposed. The proposed method minimizes the mean square error of spectral reflectance when the reflectance can be regarded as a random sequence of Gaussian mixture distribution. In computer simulations, 168 samples of spectral reflectance from a color chart are estimated from their image signals obtained by three- and six-band cameras. It is confirmed that the proposed method improves the accuracy in comparison with the conventional Wiener estimation method. OCIS codes: 100.2000, 100.3190, 300.6550, 330.1690.