Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System 超分辨率恢復
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