| 李 萍,刘以安,徐安林.基于多尺度耦合的密集残差网络红外图像增强[J].电子测量与仪器学报,2021,35(7):148-155 |
| 基于多尺度耦合的密集残差网络红外图像增强 |
| Infrared image enhancement using dense residual networkwith multi-scale coupling |
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| DOI： |
| 中文关键词: 红外图像 图像增强 深度学习 特征级联 密集残差网络 多尺度耦合 |
| 英文关键词:infrared image image enhancement deep learning feature cascade dense residual network multi-scale coupling |
| 基金项目:国家自然科学基金青年项目（21706096)资助 |
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| 中文摘要: |
| 为了提升非制冷红外热像仪的图像质量,满足低对比度弱小区域的观瞄与锁定的需求,提出了一种基于多尺度密集残
差网络的红外图像超分辨重建模型,该模型的基本框架是通过级联多个残差特征进行学习,以粗到细的方式重建高分辨率图
像。 首先提出一种多尺度跨域融合模块,通过对不同感受野的分支结果进行融合,不仅可以融合不同感受野的互补信息,还可
有助于提升梯度收敛和特征传输;然后叠加多个跨域融合模块,并采用残差特征学习进行优化,最终学习出高分辨率细节信息。
仿真实验结果表明,所提出的超分辨模型能够较好的超分辨重建效果,在微弱结构保持和点目标保持上的性能也更加突出。 所
提的模型已经在海思嵌入式深度学习平台上实现了高质量的红外增强,具有较高的工程应用价值。 |
| 英文摘要: |
| In order to improve the image quality of uncooled infrared thermal imager, and meet the needs of viewing and locking in low
contrast and dim-area, a super-resolution reconstruction model of infrared image based on multi-scale dense residual network is proposed
in this paper. The basic framework of the model is to reconstruct high-resolution image by cascading multiple residual features. Firstly,
a multi-scale cross-channel fusion module is proposed. By fusing the branch results of different receptive fields, it not only fuses the
complementary information of different receptive fields, but also helps to improve the gradient convergence and feature transmission.
Then, multiple cross-fusion modules are cascaded and optimized by residual feature learning to learn high-resolution detail information.
The simulation results show that the super-resolution model proposed in this paper can achieve better super-resolution reconstruction
effect, and has better performance in weak structure maintenance and point target maintenance. Our proposed model has achieved highquality super-resolution reconstruction on the embedded deep learning platform of Hisilicon, and has high engineering application value. |
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