Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Super-resolution using Sparse Representations over Learned Dictionaries:
  Reconstruction of Brain Structure using Electron Microscopy   A central problem in neuroscience is reconstructing neuronal circuits on the
synapse level. Due to a wide range of scales in brain architecture such
reconstruction requires imaging that is both high-resolution and
high-throughput. Existing electron microscopy (EM) techniques possess required
resolution in the lateral plane and either high-throughput or high depth
resolution but not both. Here, we exploit recent advances in unsupervised
learning and signal processing to obtain high depth-resolution EM images
computationally without sacrificing throughput. First, we show that the brain
tissue can be represented as a sparse linear combination of localized basis
functions that are learned using high-resolution datasets. We then develop
compressive sensing-inspired techniques that can reconstruct the brain tissue
from very few (typically 5) tomographic views of each section. This enables
tracing of neuronal processes and, hence, high throughput reconstruction of
neural circuits on the level of individual synapses.

27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph