تقرير
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
| العنوان: | HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery |
|---|---|
| المؤلفون: | Deudon, Michel, Kalaitzis, Alfredo, Goytom, Israel, Arefin, Md Rifat, Lin, Zhichao, Sankaran, Kris, Michalski, Vincent, Kahou, Samira E., Cornebise, Julien, Bengio, Yoshua |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning |
| الوصف: | Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery. Comment: 15 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2002.06460Test |
| رقم الانضمام: | edsarx.2002.06460 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |