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

Michel Deudon; Alfredo Kalaitzis; Israel Goytom; Md Rifat Arefin; Zhichao Lin; Kris Sankaran; Vincent Michalski; Samira E. Kahou; Julien Cornebise; Yoshua Bengio

arXiv · 2002.06460

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

Michel Deudon·Alfredo Kalaitzis·Israel Goytom·Md Rifat Arefin·Zhichao Lin·Kris Sankaran·Vincent Michalski·Samira E. Kahou·Julien Cornebise·Yoshua Bengio

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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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Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio. 2020-02-15. HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery. https://arxiv.org/abs/2002.06460

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