Deep Learning frameworks are gaining increased popularity in image processing tasks such as computational hyperspectral imaging. While these frameworks achieve state-of-the-art results in terms of reconstruction quality and run time, they often require massive databases of hyperspectral cubes for training the reconstruction algorithms. Unfortunately, such databases are usually hard to acquire due to complexity and cost considerations. To mitigate these challenges, we propose a method for generating a synthetic database of hyperspectral cubes in the visible range using a limited number of natural hyperspectral cubes, an unlimited number of RGB images, and a Generative Adversarial Network model. The suggested algorithm, dubbed SHS-GAN, is trained to get a query RGB image and to output a synthetic hyperspectral cube. While the spectral domain of the synthetic hyperspectral cube shares similar statistical properties as the natural hyperspectral cubes used in the training process, the SHS-GAN is trained to preserve the spatial characteristics of the query RGB image, whereas the R, G, B values provide an additional constraint along with the spectral domain. Our suggested framework was utilized for performing Snapshot Spectral Imaging (SSI) from a single monochromatic dispersed and diffused snapshot using the DD-Net reconstruction neural network. We demonstrate, by simulations and lab experiments, that enhancing the training database with synthetic data from the SHS-GAN improves the reconstruction quality of the hyperspectral cube. In addition, we share a new original database of more than 10,000 hyperspectral cubes of real objects of size 256x256x29 in the 420-700 nm visible range.

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SHS-GAN：自然高光谱数据库的合成增强

深度学习框架在计算高光谱成像等图像处理任务中越来越受欢迎。虽然这些框架在重建质量和运行时间方面取得了最先进的结果，但它们通常需要大量的高光谱立方体数据库来训练重建算法。不幸的是，由于复杂性和成本考虑，此类数据库通常很难获得。为了缓解这些挑战，我们提出了一种使用有限数量的自然高光谱立方体、无限数量的 RGB 图像和生成对抗网络模型生成可见范围内高光谱立方体合成数据库的方法。建议的算法，称为 SHS-GAN，经过训练以获取查询 RGB 图像并输出合成的高光谱立方体。虽然合成高光谱立方体的光谱域与训练过程中使用的自然高光谱立方体具有相似的统计特性，但 SHS-GAN 被训练以保留查询 RGB 图像的空间特征，而 R、G、B 值提供与谱域一起的附加约束。我们建议的框架用于使用 DD-Net 重建神经网络从单个单色分散和漫射快照执行快照光谱成像 (SSI)。我们通过模拟和实验室实验证明，使用来自 SHS-GAN 的合成数据增强训练数据库可以提高高光谱立方体的重建质量。此外，我们共享一个新的10多个原始数据库，