Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Deep Learning Methods for Biological Image Reconstruction
  and Enhancement   Recently, deep learning approaches have become the main research frontier for
biological image reconstruction and enhancement problems thanks to their high
performance, along with their ultra-fast inference times. However, due to the
difficulty of obtaining matched reference data for supervised learning, there
has been increasing interest in unsupervised learning approaches that do not
need paired reference data. In particular, self-supervised learning and
generative models have been successfully used for various biological imaging
applications. In this paper, we overview these approaches from a coherent
perspective in the context of classical inverse problems, and discuss their
applications to biological imaging, including electron, fluorescence and
deconvolution microscopy, optical diffraction tomography and functional
neuroimaging.

18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math