CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-11-28
Cited: 0
Clicked: 1726
Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN. Filter-cluster attention based recursive network for low-light enhancement[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1028-1044.
@article{title="Filter-cluster attention based recursive network for low-light enhancement",
author="Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="7",
pages="1028-1044",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200344"
}
%0 Journal Article
%T Filter-cluster attention based recursive network for low-light enhancement
%A Zhixiong HUANG
%A Jinjiang LI
%A Zhen HUA
%A Linwei FAN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 7
%P 1028-1044
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200344
TY - JOUR
T1 - Filter-cluster attention based recursive network for low-light enhancement
A1 - Zhixiong HUANG
A1 - Jinjiang LI
A1 - Zhen HUA
A1 - Linwei FAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 7
SP - 1028
EP - 1044
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200344
Abstract: The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images, we propose a recurrent network based on filter-cluster attention (FCA), the main body of which consists of three units: difference concern, gate recurrent, and iterative residual. The network performs multi-stage recursive learning on low-light images, and then extracts deeper feature information. To compute more accurate dependence, we design a novel FCA that focuses on the saliency of feature channels. FCA and self-attention are used to highlight the low-light regions and important channels of the feature. We also design a dense connection pyramid (DenCP) to extract the color features of the low-light inversion image, to compensate for the loss of the image’s color information. Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.
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