作者是伦敦大学学院Mullard空间科学实验室成像组，之前做过对火星图像的分辨率增强。
文章用了许多的图像处理方法获得特征和高分辨率的中间结果，最后用一个生产对抗网络获得更好的高分辨率结果。
用的数据是MISR多角度成像数据，225282个训练样本，输入275m分辨率（64*64），得到68.75m（256*256）的分辨率结果
中间整个的流程和数据的处理都没怎么看懂
The MAGiGAN SRR system is based on the
mutual shape adapted [2] features from accelerated segment test (MSA-FAST) [3] combined with
convolutional neural network (CNN) [4] feature matching (see stage 2 in Section 2.2),
adaptive least-squares correlation (ALSC) and
region growing (Gotcha) [5] (see stage 3 in Section 2.2),
partial differential equation (PDE)-based total variation (TV) regularization (GPT) [6,7] (see stage 4 in Section 2.2),
support vector machine (SVM) and
graph cut (GC)-based shadow modelling and removal [8] (see stage 1 in Section 2.2), and
the generative adversarial network (GAN) [9] based super-resolution refinement method (see stage 5 in Section 2.2).
Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System 超分辨率恢复
原文：https://www.cnblogs.com/tccbj/p/10800083.html