Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost of acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique that preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out of the box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels.

中文翻译：

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使用引导滤波的保留结构光谱反射率估计

光谱是用于各种分类问题（例如，区分材料）的非常重要的信息来源。为了降低获取此信息的成本，使用了多光谱相机。通过利用有关光谱的特性，存在几种用于从多光谱图像中估计光谱的技术。不幸的是，特别是在捕获多光谱视频时，由于视频中曝光时间有限的性质，图像会受到噪声的严重影响。因此，非常需要明确尝试降低噪声对重构频谱影响的模型。因此，提出了一种新颖的重建算法。这种新颖的估计方法基于引导滤波技术，该技术保留了基本结构，同时使用空间信息来减少噪声的影响。基于自然图像光谱的评估表明，与其他最新的空间重构方法相比，该新技术在嘈杂的场景中可产生更好的定量和主观结果。具体而言，在嘈杂的场景中，该算法将均方误差和频谱角分别降低了46％和35％。此外，表明所提出的重建技术是开箱即用的，并且不需要通过从具有九个通道的真实多光谱相机中重建光谱来进行任何校准或训练。提出的算法在嘈杂的情况下分别将均方误差和频谱角分别降低了46％和35％。此外，表明所提出的重建技术是开箱即用的，不需要通过从具有九个通道的真实多光谱相机中重建光谱来进行任何校准或训练。提出的算法在嘈杂的情况下分别将均方误差和频谱角分别降低了46％和35％。此外，表明所提出的重建技术是开箱即用的，不需要通过从具有九个通道的真实多光谱相机中重建光谱来进行任何校准或训练。