Full Text:   <5141>

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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: 6003

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xinya Wang

https://orcid.org/0000-0003-2144-9811

Jiayi Ma

https://orcid.org/0000-0003-3264-3265

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.10 P.1299-1310

http://doi.org/10.1631/FITEE.2000566


MPIN: a macro-pixel integration network for light field super-resolution


Author(s):  Xinya Wang, Jiayi Ma, Wenjing Gao, Junjun Jiang

Affiliation(s):  Electronic Information School, Wuhan University, Wuhan 430072, China; more

Corresponding email(s):   wangxinya@whu.edu.cn, jyma2010@gmail.com, wenjinggao@whu.edu.cn, junjun0595@163.com

Key Words:  Light field, Super-resolution, Macro-pixel representation


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.

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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.

MPIN:基于宏像素聚合的光场图像超分辨率网络

王歆雅1,马佳义1,高文静1,江俊君2
1武汉大学电子信息学院,中国武汉市,430072
2哈尔滨工业大学计算机科学与技术学院,中国哈尔滨市,150001
摘要:现有的大多数光场超分辨率方法不能充分利用角度信息,或者由于利用部分视图而产生不均衡的性能。为解决这些问题,本文提出一种基于宏像素表示的光场图像超分辨率聚合网络模型(称为MPIN)。该网络通过将四维光场图像重新排列成二维宏像素图像,将空间和角度信息进行耦合,从而同时恢复整张光场图像。网络利用两种特殊的卷积分别提取空间和角度信息。为充分利用空间-角度相关性,所设计的聚合残差模块融合两种信息使其相互引导,以实现角度相干性。在宏像素表示下,该网络通过扩展角度混洗层来提高宏像素图像的空间分辨率,有效避免了混叠。在合成和真实光场数据集上的大量实验表明,本文提出的方法在定性和定量上均实现了比现有方法更好的性能。此外,该方法在保持光场图像固有极线结构的同时,具有均衡性能分布的优点。

关键词:光场;超分辨率;宏像素表示

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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