Accurate Wiener Estimation by Constructing a Similar Training Set Based on Spectral Correlation

Abstract

An image is generally acquired from traditional digital camera with three channels, such as red, green, and blue. However, an RGB image cannot fully represent real scene. To accurately represent the colors in a real scene, a multi-channel camera system is used to estimate spectral reflectance. Wiener estimation is widely used methods for estimating the spectral reflectance. While simple and accurate in controlled conditions, the Wiener estimation does not perform as well with real scene data. Therefore, the adaptive Wiener estimation has been proposed to improve the performance of the Wiener estimation. It uses a similar training set that was adaptively constructed from the standard training set. In this paper, a new way of constructing similar training set is proposed by using the correlation between spectral reflectance in the standard training set and the approximated spectral reflectance by the Wiener estimation. The experimental results show that the proposed method is more accurate than the conventional Wiener estimations.