Spectral Estimation of Munsell Color Charts Using a Multi-Channel Imaging System

A practical way to capture images and estimate their spectral reflectance was proposed. For the image acquisition, a four-channel digital camera was used. Then the reflectance is represented as a linear combination of several basis vectors by singular value decomposition (SVD). After that, a neural network is trained so that it is able to approximate the relationship between the camera responses and the coefficients of basis vectors accurately. In the end the spectral reflectance of standard Munsell color patch (Matte) was estimated on the neural network and basis vectors. Results show that the reflectance of standard Munsell color patch (Matte) can be reconstructed successfully with mean of RMS which is 0.0234. Compared with linear approximation method, reconstruction of standard Munsell color patch (Matte)using this approach reduces the reconstruction error by 67%. Therefore we conclude that this approach has advantages of higher accuracy, easy implementation and adaptation, thus can be used in many applications.