Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Smoothing via Unsupervised Learning   Image smoothing represents a fundamental component of many disparate computer
vision and graphics applications. In this paper, we present a unified
unsupervised (label-free) learning framework that facilitates generating
flexible and high-quality smoothing effects by directly learning from data
using deep convolutional neural networks (CNNs). The heart of the design is the
training signal as a novel energy function that includes an edge-preserving
regularizer which helps maintain important yet potentially vulnerable image
structures, and a spatially-adaptive Lp flattening criterion which imposes
different forms of regularization onto different image regions for better
smoothing quality. We implement a diverse set of image smoothing solutions
employing the unified framework targeting various applications such as, image
abstraction, pencil sketching, detail enhancement, texture removal and
content-aware image manipulation, and obtain results comparable with or better
than previous methods. Moreover, our method is extremely fast with a modern GPU
(e.g, 200 fps for 1280x720 images). Our codes and model are released in
https://github.com/fqnchina/ImageSmoothing.

11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess