Spatio-spectral Wiener Estimation from Multispectral Images Recorded under Fluorescent Illumination

When multispectral images are recorded under fluorescent illumination with insufficient power, sometimes color estimation error as well as noise increase in the reproduced color images. This is because some of the band images gain only low S/N ratio under fluorescent illumination. To improve the image fidelity, this paper proposes to combine spatial restoration methods with color image reproduction. Several methods including spatio-spectral Wiener estimation are examined through simulations. As a result, it is confirmed that spatio-spectral Wiener estimation reduces color estimation error and noise without visually-perceived degradation of spatial resolution. Introduction Though multispectral imaging realizes high fidelity color reproduction [1-4], the expected accuracy cannot be obtained when an image is captured under fluorescent illumination with insufficient power. Except for line spectra, the spectral power of fluorescent lamps is low. Thus, when narrow band color filters of multispectral camera are used, some specific band images become very dark; that is, their S/N ratio becomes low. As a result, as well as reducing color estimation accuracy, noise becomes obvious in the reproduced images. There is no need to seriously consider such problems for conventional three-channel cameras because their color filters have wide spectral bands. In order to reduce noise on the recorded multispectral images, image restoration method such as spatial averaging operator can be effective. There are two simple ways to apply restoration methods to multispectral-image-based color reproduction: applying it before and after color estimation procedure. In addition, we can think about combining spatial restoration and color estimation to work as a single operation. In this paper, these different approaches are compared from the viewpoint of image fidelity improving when multispectral images are recorded under fluorescent illumination. In this paper, we assume that color estimation is performed based on spectral reflectance estimation by Wiener estimation. In addition, spatial Wiener filtering [5] is employed for image restoration, which is one of the effective methods of restoring images in the presence of noise. Though the both terms, Wiener estimation and Wiener filtering, are used conventionally, they are derived based on the same criteria. Therefore we can simply combine them into a spatio-spectral Wiener estimation, which can be more effective than applying them separately. Figure 1 shows four different approaches to obtain a color image from a multispectral image. The first one referred to as ‘1D’ is a conventional method, in which spectral reflectance is estimated by pixel-by-pixel Wiener estimation and the color is calculated from the estimated spectral reflectance. The next one referred to as ‘1D+2D’ consists of a color estimation followed by a two-dimensional spatial Wiener filtering. On the other hand, ‘2D+1D’ consists of a spatial Wiener filtering followed by a color image estimation. The last one referred to as ‘3D’ corresponds to a three-dimensional spatio-spectral Wiener estimation. In this paper, for the purpose of improving the reproduced image quality when multispectral images are recorded under fluorescent illumination, the effectiveness of the above-mentioned four approaches is examined. Since Wiener filtering is a kind of spatial averaging operators, it involves the degradation of spatial resolution. Therefore the degradation in spatial resolution is also evaluated.