Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts   This presentation addresses the problem of reconstructing a high-resolution
image from multiple lower-resolution snapshots captured from slightly different
viewpoints in space and time. Key challenges for solving this problem include
(i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw
(noisy) images for maximal faithfulness to native camera data, and (iii)
designing/learning an image prior (regularizer) well suited to the task. We
address these three challenges with a hybrid algorithm building on the insight
from Wronski et al. that aliasing is an ally in this setting, with parameters
that can be learned end to end, while retaining the interpretability of
classical approaches to inverse problems. The effectiveness of our approach is
demonstrated on synthetic and real image bursts, setting a new state of the art
on several benchmarks and delivering excellent qualitative results on real raw
bursts captured by smartphones and prosumer cameras.

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The categories of this paper are: ao-sci
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
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The categories of this paper are: supr-con
9
The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th