Zhuhai Fudan Innovation Institute, Zhuhai 519000, China.
School of Fashion and Textile, The Hong Kong Polytechnic University, Hong Kong, China.
Sensors (Basel). 2023 Jan 6;23(2):689. doi: 10.3390/s23020689.
Spectral reflectance reconstruction for multispectral images (such as Weiner estimation) may perform sub-optimally when the object being measured has a texture that is not in the training set. The accuracy of the reconstruction is significantly lower without training samples. We propose an improved reflectance reconstruction method based on L1-norm penalization to solve this issue. Using L1-norm, our method can provide the transformation matrix with the favorable sparse property, which can help to achieve better results when measuring the unseen samples. We verify the proposed method by reconstructing spectral reflection for four types of materials (cotton, paper, polyester, and nylon) captured by a multispectral imaging system. Each of the materials has its texture and there are 204 samples in each of the materials/textures in the experiments. The experimental results show that when the texture is not included in the training dataset, L1-norm can achieve better results compared with existing methods using colorimetric measure (i.e., color difference) and shows consistent accuracy across four kinds of materials.
多光谱图像（如 Wiener 估计）的光谱反射率重建在测量对象具有不在训练集中的纹理时可能表现不佳。没有训练样本时，重建的准确性会显著降低。我们提出了一种基于 L1 范数惩罚的改进反射率重建方法来解决这个问题。使用 L1 范数，我们的方法可以为变换矩阵提供有利的稀疏特性，这有助于在测量未见样本时获得更好的结果。我们通过重建多光谱成像系统捕获的四种材料（棉、纸、聚酯和尼龙）的光谱反射率来验证所提出的方法。每种材料都有其纹理，并且在实验中每种材料/纹理都有 204 个样本。实验结果表明，当纹理不在训练数据集中时，与使用比色测量（即色差）的现有方法相比，L1 范数可以获得更好的结果，并且在四种材料上都表现出一致的准确性。
