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CLC number: TP391.4
On-line Access: 2019-03-11
Received: 2017-02-21
Revision Accepted: 2017-06-04
Crosschecked: 2019-01-22
Cited: 0
Clicked: 5416
Automatic image enhancement by learning adaptive patch selection
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Na Li, Jian Zhang. Automatic image enhancement by learning adaptive patch selection[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 206-221.
@article{title="Automatic image enhancement by learning adaptive patch selection",
author="Na Li, Jian Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="2",
pages="206-221",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700125"
}
%0 Journal Article
%T Automatic image enhancement by learning adaptive patch selection
%A Na Li
%A Jian Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 2
%P 206-221
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700125
TY - JOUR
T1 - Automatic image enhancement by learning adaptive patch selection
A1 - Na Li
A1 - Jian Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 2
SP - 206
EP - 221
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700125
Abstract: Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.
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