Yuri Murakami

Color image reproduction based on multispectral and multiprimary imaging: experimental evaluation

Multispectral imaging is significant technology for the acquisition and display of accurate color information. Natural color reproduction under arbitrary illumination becomes possible using spectral information of both image and illumination light. In addition, multiprimary color display, i.e., using more than three primary colors, has been also developed for the reproduction of expanded color gamut, and for discounting observer metamerism. In this paper, we present the concept for the multispectral data interchange for natural color reproduction, and the experimental results using 16-band multispectral camera and 6-primary color display. In the experiment, the accuracy of color reproduction is evaluated in CIE (Delta) Ea*b* for both image capture and display systems. The average and maximum (Delta) Ea*b* = 1.0 and 2.1 in 16-band mutispectral camera system, using Macbeth 24 color patches. In the six-primary color projection display, average and maximum (Delta) Ea*b* = 1.3 and 2.7 with 30 test colors inside the display gamut. Moreover, the color reproduction results with different spectral distributions but same CIE tristimulus value are visually compared, and it is confirmed that the 6-primary display gives improved agreement between the original and reproduced colors.

Spectral reflectance estimation from multi-band image using color chart

A method for estimating a spectral reflectance image of an object is presented, in which the image of a color chart is used instead of the spectral characteristics of the camera and the illumination. Since the spectral characteristics of the color chart are the essential issue in this method, this paper shows the requirement for the color chart based on the spectral characteristics of the set of target objects. Computer simulations are performed to confirm the effectiveness of the proposed method for typical multi-band imaging systems and illuminations. In addition, an investigation is carried out to roughly estimate the amount of the error when the color chart does not satisfy the requirement completely.

Extraction of acne lesion in acne patients from multispectral images

In acne treatment, it is important to accurately evaluate the severity of Acne. The acne should be classified into several skin lesions including comedo, reddish papule, pustule, and scar. However, in some cases, a visual detection from RGB image maybe difficult for the proper evaluation of acne skin lesions. This paper proposes an extraction method using the spectral information of the various type of acne skin lesions calculated from the multispectral images (MSI) of the lesions. In the experiment, we showed the possibility of classifying acne lesion types by applying a combination of several linear discriminant functions (LDFs).

Color reproduction from low-SNR multispectral images using spatio-spectral Wiener estimation.

It is possible to capture images with high-fidelity color by using a spectrum-based color image reproduction method that estimates the spectral information of objects from a multispectral image along with information from illumination. When multispectral images do not have a sufficient signal-to-noise ratio (SNR), the accuracy of the spectral and color estimation will be reduced. To improve color estimation accuracy, this paper proposes a spatio-spectral Wiener estimation, which uses spatial correlation as well as spectral correlation. In order to show the effectiveness of the proposed method, computer simulations and an experiment are carried out using a six-channel video camera. As a result, it is assured that the proposed method improves color estimation accuracy and suppresses color noise.

Multispectral image enhancement for effective visualization

Color enhancement of multispectral images is useful to visualize the images spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

Hybrid-resolution multispectral imaging using color filter array

Hybrid-resolution multispectral imaging is a framework to acquire multispectral images through a reconstruction procedure using two types of measurement data with different spatial and spectral resolutions. In this paper, we propose a new method for such a framework on the basis of a full-resolution RGB image and the data obtained from an image sensor with a multispectral filter array (MSFA). In the proposed method, a small region of each image band is reconstructed as a linear combination of RGB images, where the weighting coefficients are determined using MSFA data. The effectiveness of the proposed approach is shown by simulations using spectral images of natural scenes.

Nonlinear estimation of spectral reflectance based on Gaussian mixture distribution for color image reproduction

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.

Digital color management in full-color holographic three-dimensional printer

We propose a new method of color management for a full-color holographic, three-dimensional (3D) printer, which produces a volume reflection holographic stereogram using red, green, and blue three-color lasers. For natural color management in the holographic 3D printer, we characterize its color reproduction characteristics based on the spectral measurement of reproduced light. Then the color conversion formula, which comprises a one-dimensional lookup table and a 3×3 matrix, was derived from the measurement data. The color reproducibility was evaluated by printing a color chart hologram, and the average CIELAB ΔE=13.19 is fairly small.

Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements

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 severalfold of the computational time of the conventional single-matrix method.

Maximum a posteriori estimation of spectral reflectance from color image and multipoint spectral measurements

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.