J Opt Soc Am A Opt Image Sci Vis. 2023 Mar 1;40(3):479-491. doi: 10.1364/JOSAA.479552.
In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground-truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in peak signal-to-noise ratio (PSNR) of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced, which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene.
本文介绍了一个合成高光谱视频数据库。由于无法记录地面真实高光谱视频,因此该数据库提供了在各种应用中评估算法的可能性。对于所有场景,都提供了深度图,以给出像素在所有空间维度中的位置以及在光谱维度中的反射率。提出了两种用于两种不同应用的新算法,以证明可以通过这个新数据库解决的应用的多样性。首先,扩展了一种跨光谱图像重建算法以利用两个连续帧之间的时间相关性。使用此高光谱数据库进行的评估表明,取决于场景,峰值信噪比 (PSNR) 增加了高达 5.6 dB。其次,引入了一种高光谱视频编码器,该编码器通过利用时间相关性扩展了现有的高光谱图像编码器。评估表明,取决于场景,可节省高达 10%的码率。