作者是倫敦大學學院Mullard空間科學實驗室成像組，之前做過對火星圖像的分辨率增強。

文章用了許多的圖像處理方法獲得特征和高分辨率的中間結果，最後用一個生產對抗網絡獲得更好的高分辨率結果。

用的數據是MISR多角度成像數據，225282個訓練樣本，輸入275m分辨率（64*64），得到68.75m（256*256）的分辨率結果

中間整個的流程和數據的處理都沒怎麽看懂

過程：

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 超分辨率恢復