Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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-res 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-res 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.
Multiple low-resolution images collectively contain more information than any individual lowresolution image, due to minor geometric displacements, e.g. shifts, rotations, atmospheric turbulence, and instrument noise.
Multi-Frame Super-Resolution (MFSR) (Tsai, 1984) aims to reconstruct hidden high-resolution details from multiple low-resolution views of the same scene.
Single Image Super-Resolution (SISR), as a special case of MFSR, has attracted much attention in the computer vision, machine learning and deep learning communities in the last 5 years, with neural networks learning complex image priors to upsample and interpolate images (Xu et al., 2014; Srivastava et al., 2015; He et al., 2016) .
However, in the meantime not much work has explored the learning of representations for the more general problem of MFSR to address the additional challenges of co-registration and fusion of multiple low-resolution images.
This paper explores how Multi-Frame Super-Resolution (MFSR) can benefit from recent advances in learning representations with neural networks.
To the best of our knowledge, this work is the first to introduce a deep-learning approach that solves the co-registration, fusion and registration-at-theloss problems in an end-to-end learning framework.
Prompting this line of research is the increasing drive towards planetary-scale Earth observation to monitor the environment and human rights violations.
Such observation can be used to inform policy, achieve accountability and direct on-the-ground action, e.g. within the framework of the Sustainable Development Goals (Jensen & Campbell, 2019) .
Nomenclature Registration is the problem of estimating the relative geometric differences between two images (e.g. due to shifts, rotations, deformations).
Fusion, in the MFSR context, is the problem of mapping multiple low-res representations into a single representation.
By coregistration, we mean the problem of registering all low-resolution views to improve their fusion.
By registration-at-the-loss, we mean the problem of registering the super-resolved reconstruction to the high-resolution ground-truth prior to computing the loss.
This gives rise to the notion of a registered loss.
Co-registration of multiple images is required for longitudinal studies of land change and environmental degradation.
The fusion of multiple images is key to exploiting cheap, high-revisit-frequency satellite imagery, but of low-resolution, moving away from the analysis of infrequent and expensive high-resolution images.
Finally, beyond fusion itself, super-resolved generation is required throughout the technical stack: both for labeling, but also for human oversight (Drexler, 2019) demanded by legal context (Harris et al., 2018) .
In this paper, we presented HighRes-net -the first deep learning approach to multi-frame superresolution that learns typical sub-tasks of MFSR in an end-to-end fashion:
(i) co-registration,
(ii) fusion,
(iii) up-sampling, and
(iv) registration-at-the-loss.
It recursively fuses a variable number of low-resolution views by learning a global fusion operator.
The overall fusion also aligns all low-resolution views with an implicit co-registration mechanism through the reference channel.
We also introduced ShiftNet-Lanczos, a network that learns to register and align the super-resolved output of HighRes-net with a high-resolution ground-truth.
Registration is important both to align multiple low-resolution inputs (co-registration) and to compute similarity metrics between shifted signals.
Our experiments suggest that an end-to-end cooperative setting (HighRes-net + ShiftNet-Lanczos) helps with training and test performance.
By design, our approach is fast to train, faster to test, and low in terms of memory-footprint by doing the bulk of the computational work (co-registration + fusion) on multiple images while maintaining their low-resolution height & width.
There is an ongoing proliferation of low-resolution yet high-revisit low-cost satellite imagery, but they often lack the detailed information of expensive high-resolution imagery.
We believe MFSR can raise its potential to NGOs and non-profits that contribute to the UN Sunstainable Development Goals.
A APPENDIX
The first deep learning approach to MFSR to solve registration, fusion, up-sampling in an end-to-end manner.