CLC number: TP312
On-line Access: 2021-10-08
Received: 2020-10-20
Revision Accepted: 2021-03-26
Crosschecked: 2021-08-31
Cited: 0
Clicked: 5829
Citations: Bibtex RefMan EndNote GB/T7714
Xinya Wang, Jiayi Ma, Wenjing Gao, Junjun Jiang. MPIN: a macro-pixel integration network for light field super-resolution[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1299-1310.
@article{title="MPIN: a macro-pixel integration network for light field super-resolution",
author="Xinya Wang, Jiayi Ma, Wenjing Gao, Junjun Jiang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="10",
pages="1299-1310",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000566"
}
%0 Journal Article
%T MPIN: a macro-pixel integration network for light field super-resolution
%A Xinya Wang
%A Jiayi Ma
%A Wenjing Gao
%A Junjun Jiang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 10
%P 1299-1310
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000566
TY - JOUR
T1 - MPIN: a macro-pixel integration network for light field super-resolution
A1 - Xinya Wang
A1 - Jiayi Ma
A1 - Wenjing Gao
A1 - Junjun Jiang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 10
SP - 1299
EP - 1310
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000566
Abstract: Most existing light field (LF) super-resolution (SR) methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views. To address these issues, we propose a novel integration network based on macro-pixel representation for the LF SR task, named MPIN. Restoring the entire LF image simultaneously, we couple the spatial and angular information by rearranging the four-dimensional LF image into a two-dimensional macro-pixel image. Then, two special convolutions are deployed to extract spatial and angular information, separately. To fully exploit spatial-angular correlations, the integration resblock is designed to merge the two kinds of information for mutual guidance, allowing our method to be angular-coherent. Under the macro-pixel representation, an angular shuffle layer is tailored to improve the spatial resolution of the macro-pixel image, which can effectively avoid aliasing. Extensive experiments on both synthetic and real-world LF datasets demonstrate that our method can achieve better performance than the state-of-the-art methods qualitatively and quantitatively. Moreover, the proposed method has an advantage in preserving the inherent epipolar structures of LF images with a balanced distribution of performance.
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