Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network

ZUO Cen; YANG Xiujie; ZHANG Jie; WANG Xuan

Volume 43 Issue 3

Apr. 2021

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Article Navigation > Infrared Technology > 2021 > 43(3): 251-257

ZUO Cen, YANG Xiujie, ZHANG Jie, WANG Xuan. Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network[J]. Infrared Technology , 2021, 43(3): 251-257.

Citation:

ZUO Cen, YANG Xiujie, ZHANG Jie, WANG Xuan. Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network[J]. Infrared Technology , 2021, 43(3): 251-257.

ZUO Cen, YANG Xiujie, ZHANG Jie, WANG Xuan. Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network[J]. Infrared Technology , 2021, 43(3): 251-257.

Citation:

ZUO Cen, YANG Xiujie, ZHANG Jie, WANG Xuan. Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network[J]. Infrared Technology , 2021, 43(3): 251-257.

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Super-resolution Enhancement of Infrared Images Using a Lightweight Dense Residual Network

1.
College of Artificial Intelligence and Big Data, Chongqing Vocational College of Electronic Engineering, Chongqing 401331, China
2.
Chongqing Wangjiang Industrial Co. Ltd., Chongqing 400071, China
3.
Chongqing Military Agency of the PLA Army, Chongqing 400050, China

Received Date: 2019-05-19
Rev Recd Date: 2020-03-23
Publish Date: 2021-04-02

Abstract

Abstract

Existing infrared-guided weapons heavily rely on operators to acquire targets, and the accuracy of acquisition is positively correlated with a target's texture details. To improve the display quality of weak small regions and meet the design requirements of miniaturization, modularization, and low-cost seekers, an image super-resolution(SR) reconstruction algorithm based on a pyramid dense residual network is proposed. The dense residual network is the basic framework of the proposed model. Through the dense connection layer and the residual network, the model can learn the non-linear mapping between images of different scales, and the multi-scale feature can be used to predict the high-frequency residual. In addition, using the deep supervision module to guide network training is conducive to the realization of SR reconstruction with a larger upper-sampling factor and improvements to its generalization ability. A large number of simulation results show that our proposed model outperforms comparison algorithms and that it has a high engineering application value.
- SR reconstruction,
- lightweight,
- infrared image,
- dense residual-network,
- loss function,
- deep supervision,

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References(22)

References

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