Appl Opt. 2022 Aug 20;61(24):7163-7172. doi: 10.1364/AO.460752.
Imaging in visible and short-wave infrared (SWIR) wavebands is essential in most remote sensing applications. However, compared to visible imaging cameras, SWIR cameras typically have lower spatial resolution, which limits the detailed information shown in SWIR images. We propose a method to reconstruct high-resolution polarization SWIR images with the help of color images using the deep learning method. The training dataset is constructed from color images, and the trained model is well suited for SWIR image reconstruction. The experimental results show the effectiveness of the proposed method in enhancing the quality of the polarized SWIR images with much better spatial resolution. Some buried spatial and polarized information may be recovered in the reconstructed SWIR images.
在可见和短波近红外（SWIR）波段的成像在大多数遥感应用中是必不可少的。然而，与可见成像相机相比，SWIR 相机通常具有较低的空间分辨率，这限制了 SWIR 图像中显示的详细信息。我们提出了一种使用深度学习方法借助彩色图像重建高分辨率偏振 SWIR 图像的方法。训练数据集是由彩色图像构建的，并且训练好的模型非常适合 SWIR 图像重建。实验结果表明，该方法在增强偏振 SWIR 图像的质量方面非常有效，具有更好的空间分辨率。在重建的 SWIR 图像中可能会恢复一些被掩埋的空间和偏振信息。
