Yang Ping, Liao Ning-fang, Song Hong
National Laboratory of Color Science and Engineering, Department of Optical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 May;29(5):1176-80.
It is still challenging to reconstruct the spectral reflectance of a surface using digital cameras under given luminance and observation conditions. A new approach to solving the problem which is based on neural network and basis vectors is proposed. At first, the spectral reflectance of the sample surface is measured by spectrometer and the response of an digital camera is recorded. 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 can be reconstructed based on the neural network and basis vectors. In the present paper, the authors reconstructed the spectrum reflectance based on neural network and basis vectors. Compared with other traditional methods, neural network expands the space of unknown function F(S) from linear functions to more general nonlinear functions, which gives more accurate estimation of the coefficients alphak and better reflectance reconstruction. 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%. Since the neural network can be implemented by Matlab neural network toolbox, this method can be easily adopted in many other cases. Therefore we conclude that this approach has advantages of higher accuracy, easy implementation and adaptation, thus can be used in many applications.
在给定的亮度和观测条件下，使用数码相机重建表面的光谱反射率仍然具有挑战性。提出了一种基于神经网络和基向量解决该问题的新方法。首先，用光谱仪测量样品表面的光谱反射率，并记录数码相机的响应。然后通过奇异值分解（SVD）将反射率表示为几个基向量的线性组合。之后，训练神经网络，使其能够准确地逼近相机响应与基向量系数之间的关系。最后，可以基于神经网络和基向量重建光谱反射率。在本文中，作者基于神经网络和基向量重建了光谱反射率。与其他传统方法相比，神经网络将未知函数F(S)的空间从线性函数扩展到更一般的非线性函数，从而对系数αk给出更准确的估计，并实现更好的反射率重建。结果表明，标准孟塞尔色卡（哑光）的反射率能够成功重建，均方根误差均值为0.0234。与线性近似方法相比，用该方法重建标准孟塞尔色卡（哑光）可将重建误差降低67%。由于神经网络可以通过Matlab神经网络工具箱实现，因此该方法可以很容易地应用于许多其他情况。因此，我们得出结论，该方法具有精度高、易于实现和适应性强等优点，可用于许多应用。
