Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Digital Camera Pipeline for Extreme Low-Light Imaging   In low-light conditions, a conventional camera imaging pipeline produces
sub-optimal images that are usually dark and noisy due to a low photon count
and low signal-to-noise ratio (SNR). We present a data-driven approach that
learns the desired properties of well-exposed images and reflects them in
images that are captured in extremely low ambient light environments, thereby
significantly improving the visual quality of these low-light images. We
propose a new loss function that exploits the characteristics of both
pixel-wise and perceptual metrics, enabling our deep neural network to learn
the camera processing pipeline to transform the short-exposure, low-light RAW
sensor data to well-exposed sRGB images. The results show that our method
outperforms the state-of-the-art according to psychophysical tests as well as
pixel-wise standard metrics and recent learning-based perceptual image quality
measures.

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The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph