Unsupervised Video Satellite Super-Resolution by Using Only a Single Video
Zhi He, Dan He, Xinyuan Li, Jiani Xu · IEEE Geoscience and Remote Sensing Letters · 2020
Recent studies have shown that deep-learning (DL)-based methods lead to improved performance in video satellite super-resolution (SR). However, the vast majority of prior work is supervised, which is restricted to artificially generated training data (e.g., predetermined bicubic downsampling). Unfortunately, in the real world, the low-resolution (LR) satellite video frames rarely obey these restrictions. To solve this problem, we resort to unsupervised learning and propose a video satellite SR method by using only a single video. The single video SR (SingleVSR) method takes advantage of the power of DL without relying on prior high-resolution (HR) and LR pairs. In the training phase, the LR frames are alternately processed by both downsampling network (i.e., NetLR) and upsampling network (i.e., NetHR). The losses obtained by LR frames and network outputs are used to optimize both NetLRand NetHR. In the testing phase, the trained NetHRis applied to generate the SR results of LR frames. In contrast to the existing video satellite SR methods, our SingleVSR does not require any assumption on degradation or any additional training data except for the single video to be tested. Experiments performed on Jilin-1 and OVS-1 satellite videos demonstrate the superiority of the proposed method.