针对现有红外图像分辨率低、质量不高的问题，提出了基于通道注意力与迁移学习的红外图像超分辨率重建方法。该方法设计了一个深度卷积神经网络，融入通道注意力机制来增强网络的学习能力，并且使用残差学习方式来减轻梯度爆炸或消失问题，加速网络的收敛。考虑到高质量的红外图像难以采集、数目不足的情况，将网络的训练分成两步：第一步使用自然图像来预训练网络模型，第二步利用迁移学习的知识，用较少数量的高质量红外图像对预训练的模型参数进行迁移微调，使模型对红外图像的重建效果更优。最后，加入多尺度细节滤波器来提升红外重建图像的视觉效果。在Set5、Set14数据集以及红外图像上的实验表明，融入通道注意力机制和残差学习方法，均能提升超分辨率重建的效果，迁移微调能很好地解决红外样本数量不足的问题，而多尺度细节提升滤波则能提升重建图像的细节，增大信息量。
基于通道注意力与迁移学习的红外图像超分辨率重建算法
作者单位信息
通信作者： 1402198087@qq.com
出版日期：2021年1月15日
摘要
参考文献
[1] Su H, Zhou J, Zhang Z H. Survey of super-resolution image reconstruction methods[J]. Acta Autom Sin, 2013, 39(8): 1202–1213.
苏衡, 周杰, 张志浩. 超分辨率图像重建方法综述[J]. 自动化学报, 2013, 39(8): 1202–1213.
[2] Bätz M, Eichenseer A, Seiler J, et al. Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches[C]//Proceedings of 2015 IEEE International Conference on Image Processing (ICIP), 2015: 58–62.
[3] Kim K I, Kwon Y. Single-image super-resolution using sparse regression and natural image prior[J]. IEEE Trans Pattern Anal Mach Intell, 2010, 32(6): 1127–1133.
[4] Lian Q S, Zhang W. Image super-resolution algorithms based on sparse representation of classified image patches[J]. Acta Electron Sin, 2012, 40(5): 920–925.
练秋生, 张伟. 基于图像块分类稀疏表示的超分辨率重构算法[J]. 电子学报, 2012, 40(5): 920–925.
[5] Xiao J S, Liu E Y, Zhu L, et al. Improved image super-resolution algorithm based on convolutional neural network[J]. Acta Opt Sin, 2017, 37(3): 0318011.
肖进胜, 刘恩雨, 朱力, 等. 改进的基于卷积神经网络的图像超分辨率算法[J]. 光学学报, 2017, 37(3): 0318011.
[6] Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections[J]. J Opt Soc Am A, 1989, 6(11): 1715–1726.
[7] Irani M, Peleg S. Improving resolution by image registration[J]. CVGIP: Graph Models Image Process, 1991, 53(3): 231–239.
[8] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding[C]//Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004: 275–282.
[9] Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Trans Image Process, 2010, 19(11): 2861–2873.
[10] Dong C, Loy C C, He K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38(2): 295–307.
[11] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 391–407.
[12] Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1874–1883.
[13] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4681–4690.
[14] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Trans Knowl Data Eng, 2010, 22(10): 1345–1359.
[15] Xu Z, Qu C W, He L Q. SAR target super-resolution based on transfer learning[J]. Acta Aeronaut Astronaut Sin, 2015, 36(6): 1940–1952.
徐舟, 曲长文, 何令琪. 基于迁移学习的SAR目标超分辨重建[J]. 航空学报, 2015, 36(6): 1940–1952.
[16] Yanai K, Kawano Y. Food image recognition using deep convolutional network with pre–training and fine–tuning[C]//Proceedings of 2015 IEEE International Conference on Multimedia & Expo Workshops, 2015: 1–6.
[17] Du B, Xiong W, Wu J, et al. Stacked convolutional denoising auto-encoders for feature representation[J]. IEEE Trans Cybern, 2017, 47(4): 1017–1027.
[18] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7132–7141.
[19] Kim Y, Koh Y J, Lee C, et al. Dark image enhancement based onpairwise target contrast and multi-scale detail boosting[C]//Proceedings of 2015 IEEE International Conference on Image Processing, 2015: 1404–1408.
[20] Kingma D P, Ba J. Adam: a method for stochastic optimization[Z]. arXiv: 1412.6980, 2014.
[21] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Process Lett, 2013, 20(3): 209–212.
[22] Shao X, Zeng T Y, Wang Z H. No-reference quality assessment method for printed image based on NIQE[J]. Packaging J, 2016, 8(4): 35–39.
邵雪, 曾台英, 汪祖辉. 一种基于NIQE的印刷图像无参考质量评价方法[J]. 包装学报, 2016, 8(4): 35–39.
基金项目：
国家自然科学基金面上项目(61471154，61876057)；中央高校基本科研业务费专项资金资助项目(JZ2018YYPY0287)
导出参考文献，格式为：
引用本文：
孙锐, 章晗, 程志康, 等. 基于通道注意力与迁移学习的红外图像超分辨率重建算法[J]. 光电工程, 2021, 48(1): 200045.
下一篇：基于深度学习的门机抓斗检测方法
