Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
2021
Recently, satellites with high temporal resolution have fostered wide
attention in various practical applications. Due to limitations of bandwidth
and hardware cost, however, the spatial resolution of such satellites is
considerably low, largely limiting their potentials in scenarios that require
spatially explicit information. To improve image resolution, numerous
approaches based on training low-high resolution pairs have been proposed to
address the super-resolution (SR) task. Despite their success, however,
low/high spatial resolution pairs are usually difficult to obtain in satellites
with a high temporal resolution, making such approaches in SR impractical to
use. In this paper, we proposed a new unsupervised learning framework, called
"MIP", which achieves SR tasks without low/high resolution image pairs. First,
random noise maps are fed into a designed generative adversarial network (GAN)
for reconstruction. Then, the proposed method converts the reference image to
latent space as the migration image prior. Finally, we update the input noise
via an implicit method, and further transfer the texture and structured
information from the reference image. Extensive experimental results on the
Draper dataset show that MIP achieves significant improvements over
state-of-the-art methods both quantitatively and qualitatively. The proposed
MIP is open-sourced at http://github.com/jiaming-wang/MIP.
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