HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

• URL: http://arxiv.org/abs/2002.06460v1
• Date: Sat, 15 Feb 2020 22:17:47 GMT
• Title: HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
• Authors: Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio
• Abstract summary: Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem. This is important for satellite monitoring of human impact on the planet. We present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion.
• Score: 55.253395881190436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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